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: "discrete power control stochastic game on the multiple access channel"
  Abstract: We consider the scenario where multiple users send information to a single receiver over a common channel. Each user is aware of only his channel fading state and has statistical knowledge of the fading process of the other users. The fading process of each user is modeled as ergodic finite state Markov chains. We consider two problems. The first problem is the non-cooperative game where the receiver decodes the the message of each transmitter by considering the signals of the other users as noise. In the second problem, the receiver employs a successive interference decoder to decode the messages of the users. The two problems are posed as stochastic games, the first one as a non-cooperative game where the aim is to maximize the expected time average throughput of each user in a selfish manner. In the second problem, the aim is maximize the expected time average sum throughput of the system. We show the existence of Nash equilibrium policies for both the games and give a best response algorithm to find them for both the games. Our analysis begins first by showing that the second game is the potential game of the first one, hence the equilibria policies for both the games are same. We then analyze the first game at large but finite number of users. We show there exist a natural number such that when the number of users exceed this number, then the equilibrium policies are unique. Furthermore the equilibrium policies of the users are invariant as long as the number of users remain above the latter threshold. These policies are then shown to achieve the global maximum of the expected time average sum throughput of the second game. We give a algorithm which is linear in number of users to compute these threshold equilibrium policies.

Connected through hyperedge HE-1 (induced by source paper "large player games on wireless networks"):
  with members:
    - "mean field energy games in wireless networks"
    - "optimal linear precoding strategies for wideband noncooperative systems based on game theory part i nash equilibria"
    - "optimal linear precoding strategies for wideband non cooperative systems based on game theory part ii algorithms"

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.SY(Systems and Control)