human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "approximate best response dynamics in random interference games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep multi user reinforcement learning for distributed dynamic spectrum access")
  Member "asymptotically optimal distributed channel allocation a competitive game theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "asymptotically optimal resource block allocation with limited feedback")
  Member "fully distributed optimal channel assignment for open spectrum access":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed learning for channel allocation over a shared spectrum")
  Member "on the nash equilibria in decentralized parallel interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed algorithms for complete and partial information games on interference channels")
  Member "learning equilibria with partial information in decentralized wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interference coordination via power domain channel estimation")
  Member "shannon meets nash on the interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "strategic communication with side information at the decoder")
  Member "game theory and the frequency selective interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "channel equilibrium networks for learning deep representation")
  Member "the mimo iterative waterfilling algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed power control with partial channel state information performance characterization and design")

Question: Please tell me which class the source paper 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)