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: "approximate best response dynamics in random interference games"
  Abstract: In this paper, we develop a novel approach to the convergence of best-response dynamics for the family of interference games. Interference games represent the fundamental resource allocation conflict between users of the radio spectrum. In contrast to congestion games, interference games are generally not potential games. Therefore, proving the convergence of the best-response dynamics to a Nash equilibrium in these games requires new techniques. We suggest a model for random interference games, based on the long-term fading governed by the players’ geometry. Our goal is to prove convergence of the approximate best-response dynamics with high probability with respect to the randomized game. We embrace the asynchronous model in which the acting player is chosen at each stage at random. In our approximate best-response dynamics, the action of a deviating player is chosen at random among all the approximately best ones. We show that with high probability, with respect to the players’ geometry and asymptotically with the number of players, each action increases the expected social-welfare (sum of achievable rates). Hence, the induced sum-rate process is a submartingale. Based on the martingale convergence theorem, we prove convergence of the strategy profile to an approximate Nash equilibrium with good performance for asymptotically almost all interference games. We use the Markovity of the induced sum-rate process to provide probabilistic bounds on the convergence time. Finally, we demonstrate our results in simulated examples.

Connected through hyperedge HE-1 (induced by source paper "deep multi user reinforcement learning for distributed dynamic spectrum access"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement one shot learning for artificially intelligent classification systems"
    - "deep reinforcement learning an overview"
    - "active anomaly detection in heterogeneous processes"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "deep reinforcement learning radio control and signal detection with kerlym a gym rl agent"

Connected through hyperedge HE-2 (induced by source paper "game theoretic dynamic channel allocation for frequency selective interference channels"):
  with members:
    - "asymptotically optimal distributed channel allocation a competitive game theoretic approach"
    - "fully distributed optimal channel assignment for open spectrum access"
    - "on the nash equilibria in decentralized parallel interference channels"
    - "learning equilibria with partial information in decentralized wireless networks"
    - "shannon meets nash on the interference channel"
    - "game theory and the frequency selective interference channel"
    - "the mimo iterative waterfilling algorithm"
    - "competitive design of multiuser mimo systems based on game theory a unified view"

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.GT(Computer Science and Game Theory)