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: "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
  Abstract: In this paper, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality-of-experience (QoE) of wireless devices in a cloud radio access network (CRAN) is studied. In the considered model, the network can leverage human-centric information such as users' visited locations, requested contents, gender, job, and device type to predict the content request distribution and mobility pattern of each user. Then, given these behavior predictions, the proposed approach seeks to find the user-UAV associations, the optimal UAVs' locations, and the contents to cache at UAVs. This problem is formulated as an optimization problem whose goal is to maximize the users' QoE while minimizing the transmit power used by the UAVs. To solve this problem, a novel algorithm based on the machine learning framework of conceptor-based echo state networks (ESNs) is proposed. Using ESNs, the network can effectively predict each user's content request distribution and its mobility pattern when limited information on the states of users and the network is available. Based on the predictions of the users' content request distribution and their mobility patterns, we derive the optimal user-UAV association, optimal locations of the UAVs as well as the content to cache at UAVs. Simulation results using real pedestrian mobility patterns from BUPT and actual content transmission data from Youku show that the proposed algorithm can yield 40% and 61% gains, respectively, in terms of the average transmit power and the percentage of the users with satisfied QoE compared to a benchmark algorithm without caching and a benchmark solution without UAVs.

Connected through hyperedge HE-1 (induced by source paper "on the age of information in internet of things systems with correlated devices"):
  with members:
    - "age of information analysis for dynamic spectrum sharing"
    - "minimizing the age of information of cognitive radio based iot systems under a collision constraint"
    - "sampling for remote estimation through queues age of information and beyond"
    - "a reinforcement learning framework for optimizing age of information in rf powered communication systems"
    - "age of information in multiple sensing of a single source"
    - "status from a random field how densely should one update"
    - "age of information in multiple sensing"
    - "minimizing the age of information in a csma environment"

Connected through hyperedge HE-2 (induced by source paper "voting based multi agent reinforcement learning for intelligent iot"):
  with members:
    - "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "scalable bilinear pi learning using state and action features"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "efficient 3d aerial base station placement considering users mobility by reinforcement learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"

Connected through hyperedge HE-3 (induced by source paper "backhaul aware optimization of uav base station location and bandwidth allocation for profit maximization"):
  with members:
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "uav base station location optimization for next generation wireless networks overview and future research directions"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "spatial configuration of agile wireless networks with drone bss and user in the loop"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "user association and bandwidth allocation for terrestrial and aerial base stations with backhaul considerations"
    - "backhaul aware robust 3d drone placement in 5g wireless networks"

Connected through hyperedge HE-4 (induced by source paper "stacked auto encoder based deep reinforcement learning for online resource scheduling in large scale mec networks"):
  with members:
    - "deep reinforcement learning for online computation offloading in wireless powered mobile edge computing networks"
    - "unified offloading decision making and resource allocation in me ran"
    - "deep reinforcement learning that matters"
    - "a survey on mobile edge computing the communication perspective"
    - "prioritized experience replay"
    - "deep reinforcement learning with double q learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "uav trajectory optimization for time constrained applications"):
  with members:
    - "overcoming endurance issue uav enabled communications with proactive caching"
    - "compact provably good lps for orienteering and regret bounded vehicle routing"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"

Connected through hyperedge HE-6 (induced by source paper "uav communications for 5g and beyond recent advances and future trends"):
  with members:
    - "comparison of limited feedback schemes for noma transmission in mmwave drone networks"
    - "energy optimization for cellular connected multi uav mobile edge computing systems with multi access schemes"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "resource allocation for solar powered uav communication systems"
    - "cellular connected uav potentials challenges and promising technologies"
    - "real time optimal resource allocation for embedded uav communication systems"
    - "optimal 3d trajectory design and resource allocation for solar powered uav communication systems"
    - "minimum throughput maximization in uav aided wireless powered communication networks"

Connected through hyperedge HE-7 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "a graph neural network approach for scalable wireless power control"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "vehicular edge computing via deep reinforcement learning"
    - "openei an open framework for edge intelligence"
    - "uav communications for 5g and beyond recent advances and future trends"

Connected through hyperedge HE-8 (induced by source paper "energy efficient resource allocation in uav enabled mobile edge computing networks"):
  with members:
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "liquid state machine learning for resource and cache management in lte u unmanned aerial vehicle uav networks"
    - "uav aided offloading for cellular hotspot"
    - "joint altitude beamwidth location and bandwidth optimization for uav enabled communications"
    - "stochastic optimization and control framework for 5g network slicing with effective isolation"
    - "joint energy minimization and resource allocation in c ran with mobile cloud"
    - "placement optimization of uav mounted mobile base stations"

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)