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 "distributed uplink resource allocation in cognitive radio networks part ii equilibria and algorithms for joint access point selection and power allocation"):
  Abstract: The main objective of this two part paper is to formulate and address the problem of distributed uplink resource allocation in multi-carrier cognitive rad io networks (CRN) with multiple Access Points (APs). When the APs operate on non-overlapping spectrum bands, such problem is essentially a joint spectrum decision and spectrum sharing problem. In this network, the cognitive users (CUs) are endowed with greater flexibility than the single AP network we consid ered in the first part of the paper [2]: they can optimize their uplink transmission rates by means of: 1) association to a suitable AP and 2) sharing the set of channels that belong to this AP with other CUs associated with this AP. Clearly these two steps are interdependent, and our objective is to devise suitable algorithms by which the CUs can perform these two steps in a distributed and efficient fashion. In the first part of this paper, we have studied solely the spec trum sharing aspect of the above problem, and proposed algorithms for the CUs in the single AP network to efficiently share the spectrum. In this second part of the paper, we build upon our previous understanding of the single AP network, and formulate the joint spectrum decision and spectrum sharing problem in a multiple AP network into a non-cooperative game, in which the feasible strategy of a player contains a discrete variable (the AP/spectrum decision) and a continuous vector (the power allocation among multiple channels). The structure of the game is hence very different from most non-cooperative spectrum management game proposed in the literature. We provide characterization of the Nash Equilibrium (NE) of this game, and

Includes members:
  Member "distributed uplink resource allocation in cognitive radio networks part i equilibria and algorithms for power allocation":
    also connected through other hyperedges:
      (none)
  Member "optimal linear precoding strategies for wideband noncooperative systems based on game theory part i nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "large player games on wireless networks")

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)