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: "liquid state machine learning for resource and cache management in lte u unmanned aerial vehicle uav networks"
  Abstract: In this paper, the problem of joint caching and resource allocation is investigated for a network of cache-enabled unmanned aerial vehicles (UAVs) that service wireless ground users over the LTE licensed and unlicensed (LTE-U) bands. The considered model focuses on users that can access both licensed and unlicensed bands while receiving contents from either the cache units at the UAVs directly or via content server-UAV-user links. This problem is formulated as an optimization problem which jointly incorporates user association, spectrum allocation, and content caching. To solve this problem, a distributed algorithm based on the machine learning framework of liquid state machine (LSM) is proposed. Using the proposed LSM algorithm, the cloud can predict the users' content request distribution while having only limited information on the network's and users' states. The proposed algorithm also enables the UAVs to autonomously choose the optimal resource allocation strategies that maximize the number of users with stable queues depending on the network states. Based on the users' association and content request distributions, the optimal contents that need to be cached at UAVs as well as the optimal resource allocation are derived. Simulation results using real datasets show that the proposed approach yields up to 33.3% and 50.3% gains, respectively, in terms of the number of users that have stable queues compared to two baseline algorithms: Q-learning with cache and Q-learning without cache. The results also show that LSM significantly improves the convergence time of up to 33.3% compared to conventional learning algorithms such as Q-learning.

Connected through hyperedge HE-1 (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"
    - "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"
    - "joint trajectory and communication design for multi uav enabled wireless networks"

Connected through hyperedge HE-2 (induced by source paper "joint access and backhaul resource management in satellite drone networks a competitive market approach"):
  with members:
    - "exploiting noma for uav communications in large scale cellular networks"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "backhaul aware placement of a uav bs with bandwidth allocation for user centric operation and profit maximization"
    - "wireless communication using unmanned aerial vehicles uavs optimal transport theory for hover time optimization"
    - "beam tracking for uav mounted satcom on the move with massive antenna array"
    - "user association and bandwidth allocation for terrestrial and aerial base stations with backhaul considerations"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"

Connected through hyperedge HE-3 (induced by source paper "optimized trajectory design in uav based cellular networks for 3d users a double q learning approach"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "joint altitude beamwidth location and bandwidth optimization for uav enabled communications"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "uav enabled wireless power transfer trajectory design and energy optimization"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"

Connected through hyperedge HE-4 (induced by source paper "predictive deployment of uav base stations in wireless networks machine learning meets contract theory"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "uav aided offloading for cellular hotspot"
    - "energy minimization for wireless communication with rotary wing uav"
    - "on demand deployment of multiple aerial base stations for traffic offloading and network recovery"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "artificial neural networks based machine learning for wireless networks a tutorial"

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)