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: "competitive spectrum management with incomplete information"
  Abstract: An important issue in wireless communication is the interaction between selfish independent wireless communication systems operating in the same frequency band. Due to the selfish nature of each system, this interaction is well modeled as a strategic game where each player (system) behaves to maximize its own utility. This paper studies an interference game where each system (player) has incomplete information about the other player's channel conditions. Using partial information, players choose between frequency division multiplexing (FDM) strategy and full spread (FS) strategy where power is spread across the transmission band. An important notion in game theory is the Nash equilibrium (NE) which represents a steady point in the game; that is, each player can only lose by unilaterally deviating from it. A trivial Nash equilibrium point in this game is where players mutually choose FS and interfere with each other. This point may lead to poor spectrum utilization from a global network point of view and even for each user individually. In this paper, we provide a closed form expression for a nonpure-FS e -Nash equilibrium point; i.e., an equilibrium point where players choose FDM for some channel realizations and FS for the others. To reach this point, the only instantaneous channel state information (CSI) required by each user is its own interference-to-signal ratio. We show that operating in this nonpure-FS e-Nash equilibrium point increases each user's throughput and therefore improves the spectrum utilization, and demonstrate that this performance gain can be substantial. Finally, important insights are provided into the behavior of selfish and rational wireless users as a function of the channel parameters such as fading probabilities, the interference-to-signal ratio.

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)