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: "distributed uplink resource allocation in cognitive radio networks part i equilibria and algorithms for power allocation"
  Abstract: Spectrum management has been identified as a crucial step tow ards enabling the technology of a cognitive radio network (CRN). Most of the current works dealing with spectrum management in the CRN focus on a single task of the problem, e.g., spectrum sensing, spectrum decision, spectrum sharing or spectrum mobility. In this two-part paper, we argue that for certain network configurations, jointly performing several tasks of the spectrum management improves the spectrum efficiency. Specifically, our aim is to study the uplink resource management problem in a CRN where there exist multiple cognitive users (CUs) and access points (APs). The CUs, in order to maximize their uplink transmission rates, have to associate to a suitable AP (spectrum decision), and to share the channels used by this AP with other CUs (spectrum sharing). These tasks are clearly interdependent, and the problem of how they should be carried out efficiently and in a distributed manner is still o pen in the literature. In this first part of the paper, we focus on the problem of spect rum sharing in a multi-channel CRN with a single AP. The insight gained from the analysis of this simpler network is used as the building block for analyzing the multiple AP network in the second part of this paper. We formulate the single AP spectrum sharing problem into a non-cooperative power allocation game, in which individual CUs aim at maximizing their transmission rate by finding the suitabl e power allocation on the available channels. Interestingly, we discover that the set of equilibrium solu tions of this game possesses the following optimality properties: 1) any equilibrium solution is the o ptimum input power allocation scheme in the sense that it maximizes the sum rate of the network if joint decoding at the AP is employed;

Connected through hyperedge HE-1 (induced by source paper "distributed uplink resource allocation in cognitive radio networks part ii equilibria and algorithms for joint access point selection and power allocation"):
  with members:
    - "optimal linear precoding strategies for wideband noncooperative systems based on game theory part i nash equilibria"

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)