human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "interference management for cellular connected uavs a deep reinforcement learning approach"
  Abstract: In this paper, an interference-aware path planning scheme for a network of cellular-connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at achieving a tradeoff between maximizing energy efficiency and minimizing both wireless latency and the interference caused on the ground network along its path. The problem is cast as a dynamic game among UAVs. To solve this game, a deep reinforcement learning algorithm, based on echo state network (ESN) cells, is proposed. The introduced deep ESN architecture is trained to allow each UAV to map each observation of the network state to an action, with the goal of minimizing a sequence of time-dependent utility functions. Each UAV uses the ESN to learn its optimal path, transmission power, and cell association vector at different locations along its path. The proposed algorithm is shown to reach a subgame perfect Nash equilibrium upon convergence. Moreover, an upper bound and a lower bound for the altitude of the UAVs are derived thus reducing the computational complexity of the proposed algorithm. The simulation results show that the proposed scheme achieves better wireless latency per UAV and rate per ground user (UE) while requiring a number of steps that are comparable to a heuristic baseline that considers moving via the shortest distance toward the corresponding destinations. The results also show that the optimal altitude of the UAVs varies based on the ground network density and the UE data rate requirements and plays a vital role in minimizing the interference level on the ground UEs as well as the wireless transmission delay of the UAV.

Connected through hyperedge HE-1 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning"):
  with members:
    - "ieee 802 11be extremely high throughput the next generation of wi fi technology beyond 802 11ax"
    - "deep reinforcement learning for resource management in network slicing"
    - "experience driven networking a deep reinforcement learning based approach"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "progressive neural architecture search"
    - "hindsight experience replay"
    - "a deep reinforcement learning based framework for content caching"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"

Connected through hyperedge HE-2 (induced by source paper "online maneuver design for uav enabled noma systems via reinforcement learning"):
  with members:
    - "fundamental rate limits of uav enabled multiple access channel with trajectory optimization"
    - "path design for cellular connected uav with reinforcement learning"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "deep learning in physical layer communications"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "placement optimization of uav mounted mobile base stations"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"
    - "reinforcement learning based resource allocation in fog radio access networks for various iot environments"

Connected through hyperedge HE-4 (induced by source paper "mobility in the sky performance and mobility analysis for cellular connected uavs"):
  with members:
    - "capacity and coverage enhancement using long endurance tethered airborne base stations"
    - "trajectory design for cellular connected uav under outage duration constraint"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "cellular connectivity for uavs network modeling performance analysis and design guidelines"
    - "on the 3 d placement of airborne base stations using tethered uavs"
    - "trajectory optimization for cellular connected uav under outage duration constraint"
    - "drones in distress a game theoretic countermeasure for protecting uavs against gps spoofing"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"

Connected through hyperedge HE-5 (induced by source paper "cellular connectivity for uavs network modeling performance analysis and design guidelines"):
  with members:
    - "cellular uav to uav communications"
    - "a brief history on the theoretical analysis of dense small cell wireless networks"
    - "key technologies and system trade offs for detection and localization of amateur drones"
    - "supporting uav cellular communications through massive mimo"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "mobile networks connected drones field trials simulations and design insights"
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"

Connected through hyperedge HE-6 (induced by source paper "cognitive uav communication via joint maneuver and power control"):
  with members:
    - "capacity characterization of uav enabled two user broadcast channel"
    - "cellular connected uav uplink association power control and interference coordination"
    - "optimized path planning for inspection by unmanned aerial vehicles swarm with energy constraints"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "energy minimization for wireless communication with rotary wing uav"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "an overview of 3gpp release 15 study on enhanced lte support for connected drones"

Connected through hyperedge HE-7 (induced by source paper "hybrid offline online design for uav enabled data harvesting in probabilistic los channel"):
  with members:
    - "average peak age of information minimization in uav assisted iot networks"
    - "efficient local map search algorithms for the placement of flying relays"
    - "deep reinforcement learning for minimizing age of information in uav assisted networks"
    - "3d trajectory optimization in rician fading for uav enabled data harvesting"
    - "path design for cellular connected uav with reinforcement learning"
    - "age optimal trajectory planning for uav assisted data collection"
    - "local map assisted positioning for flying wireless relays"
    - "learning to communicate in uav aided wireless networks map based approaches"

Connected through hyperedge HE-8 (induced by source paper "uplink non orthogonal multiple access for uav communications"):
  with members:
    - "energy minimization for wireless communication with rotary wing uav"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "uav enabled communication using noma"
    - "multiple antenna aided noma in uav networks a stochastic geometry approach"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "uplink cooperative noma for cellular connected uav"
    - "placement optimization of uav mounted mobile base stations"
    - "joint trajectory and communication design for multi uav enabled wireless networks"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)