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: "distributed learning for channel allocation over a shared spectrum"
  Abstract: Channel allocation is the task of assigning channels to users such that some objective (e.g., sum-rate) is maximized. In centralized networks such as cellular networks, this task is carried by the base station which gathers the channel state information (CSI) from the users and computes the optimal solution. In distributed networks such as ad-hoc and device-to-device (D2D) networks, no base station exists and conveying global CSI between users is costly or simply impractical. When the CSI is time varying and unknown to the users, the users face the challenge of both learning the channel statistics online and converge to a good channel allocation. This introduces a multi-armed bandit (MAB) scenario with multiple decision makers. If two users or more choose the same channel, a collision occurs and they all receive zero reward. We propose a distributed channel allocation algorithm that each user runs and converges to the optimal allocation while achieving an order optimal regret of O\left(\log T\right). The algorithm is based on a carrier sensing multiple access (CSMA) implementation of the distributed auction algorithm. It does not require any exchange of information between users. Users need only to observe a single channel at a time and sense if there is a transmission on that channel, without decoding the transmissions or identifying the transmitting users. We demonstrate the performance of our algorithm using simulated LTE and 5G channels.

Connected through hyperedge HE-1 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "machine learning in iot security current solutions and future challenges"
    - "toward intelligent vehicular networks a machine learning framework"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning for radio resource allocation in multi cell networks"
    - "applications of deep reinforcement learning in communications and networking a survey"

Connected through hyperedge HE-2 (induced by source paper "game of thrones fully distributed learning for multi player bandits"):
  with members:
    - "coordinated exploration in concurrent reinforcement learning"
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "the effect of communication on noncooperative multiplayer multi armed bandit problems"
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "distributed clustering of linear bandits in peer to peer networks"
    - "delay and cooperation in nonstochastic bandits"
    - "multi user lax communications a multi armed bandit approach"
    - "multi player bandits a musical chairs approach"

Connected through hyperedge HE-3 (induced by source paper "distributed energy efficient channel allocation"):
  with members:
    - "asymptotically optimal resource block allocation with limited feedback"
    - "game theoretic dynamic channel allocation for frequency selective interference channels"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "distributed channel assignment in cognitive radio networks stable matching and walrasian equilibrium"
    - "matching theory for future wireless networks fundamentals and applications"
    - "fully distributed optimal channel assignment for open spectrum access"
    - "on optimizing energy efficiency in multi radio multi channel wireless networks"
    - "framework for link level energy efficiency optimization with informed transmitter"

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)