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: "machine learning for wireless connectivity and security of cellular connected uavs"
  Abstract: Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be integrated into future cellular networks as new aerial mobile users. Providing cellular connectivity to UAVs will enable a myriad of applications ranging from online video streaming to medical delivery. However, to enable a reliable wireless connectivity for the UAVs as well as a secure operation, various challenges need to be addressed such as interference management, mobility management and handover, cyber-physical attacks, and authentication. In this paper, the goal is to expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAV-based real-time multimedia streaming, and UAV-enabled intelligent transportation systems. To address such challenges, artificial neural network (ANN) based solution schemes are introduced. The introduced approaches enable the UAVs to adaptively exploit the wireless system resources while guaranteeing a secure operation, in real-time. Preliminary simulation results show the benefits of the introduced solutions for each of the aforementioned cellular-connected UAV application use case.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for minimizing age of information in uav assisted networks"):
  with members:
    - "age optimal trajectory planning for uav assisted data collection"
    - "optimized path planning for inspection by unmanned aerial vehicles swarm with energy constraints"
    - "ultra reliable low latency vehicular networks taming the age of information tail"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "throughput maximization for uav enabled wireless powered communication networks"
    - "optimizing age of information in wireless networks with perfect channel state information"
    - "average age of information with hybrid arq under a resource constraint"
    - "update or wait how to keep your data fresh"

Connected through hyperedge HE-2 (induced by source paper "drones in distress a game theoretic countermeasure for protecting uavs against gps spoofing"):
  with members:
    - "dynamic mobility aware interference avoidance for aerial base stations in cognitive radio networks"
    - "a game of drones cyber physical security of time critical uav applications with cumulative prospect theory perceptions and valuations"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "cyber physical security and safety of autonomous connected vehicles optimal control meets multi armed bandit learning"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "single controller stochastic games for optimized moving target defense"

Connected through hyperedge HE-3 (induced by source paper "machine learning for intelligent authentication in 5g and beyond wireless networks"):
  with members:
    - "learning aided physical layer authentication as an intelligent process"
    - "analysis of channel based user authentication by key less and key based approaches"
    - "machine learning methods for attack detection in the smart grid"

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)