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: "game theoretic dynamic channel allocation for frequency selective interference channels"
  Abstract: We consider the problem of distributed channel allocation in large networks under the frequency-selective interference channel. Performance is measured by the weighted sum of achievable rates. Our proposed algorithm is a modified Fictitious Play algorithm that can be implemented distributedly and its stable points are the pure Nash equilibria of a given game. Our goal is to design a utility function for a non-cooperative game such that all of its pure Nash equilibria have close to optimal global performance. This will make the algorithm close to optimal while requiring no communication between users. We propose a novel technique to analyze the Nash equilibria of a random interference game, determined by the random channel gains. Our analysis is asymptotic in the number of users. First we present a natural non-cooperative game where the utility of each user is his achievable rate. It is shown that, asymptotically in the number of users and for strong enough interference, this game exhibits many bad equilibria. Then we propose a novel non-cooperative M Frequency-Selective Interference Channel Game (M-FSIG), as a slight modification of the former, where the utility of each user is artificially limited. We prove that even its worst equilibrium has asymptotically optimal weighted sum-rate for any interference regime and even for correlated channels. This is based on an order statistics analysis of the fading channels that is valid for a broad class of fading distributions (including Rayleigh, Rician, m-Nakagami and more). We carry out simulations that show fast convergence of our algorithm to the proven asymptotically optimal pure Nash equilibria.

Connected through hyperedge HE-1 (induced by source paper "distributed learning for channel allocation over a shared spectrum"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "game of thrones fully distributed learning for multi player bandits"
    - "proactive resource management for lte in unlicensed spectrum a deep learning perspective"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "propagation path loss models for 5g urban micro and macro cellular scenarios"
    - "multi player bandits a musical chairs approach"
    - "distributed channel assignment in cognitive radio networks stable matching and walrasian equilibrium"

Connected through hyperedge HE-2 (induced by source paper "sub channel allocation for device to device underlaying full duplex mmwave small cells using coalition formation games"):
  with members:
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "full duplex amplify and forward relaying power and location optimization"
    - "joint power allocation and beamforming for non orthogonal multiple access noma in 5g millimeter wave communications"
    - "full duplex millimeter wave communication"
    - "leveraging social communities for optimizing cellular device to device communications"
    - "on the spectral efficiency of massive mimo systems with low resolution adcs"
    - "exploiting device to device communications in joint scheduling of access and backhaul for mmwave small cells"
    - "efficiency resource allocation for device to device underlay communication systems a reverse iterative combinatorial auction based approach"

Connected through hyperedge HE-3 (induced by source paper "asymptotically optimal resource block allocation with limited feedback"):
  with members:
    - "asymptotically optimal distributed channel allocation a competitive game theoretic approach"
    - "fully distributed optimal channel assignment for open spectrum access"

Connected through hyperedge HE-4 (induced by source paper "distributed energy efficient channel allocation"):
  with members:
    - "asymptotically optimal resource block allocation with limited feedback"
    - "distributed learning for channel allocation over a shared spectrum"
    - "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)