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 "a potential game for power and frequency allocation in large scale wireless networks"):
  Abstract: In this paper we analyze power and frequency allocation in wireless networks through potential games. Potential games are used frequently in the literature for this purpose due to their desirable properties, such as convergence and stability. However, potential games usually assume massive message passing to obtain the necessary neighbor information at each user to achieve these properties. In this paper we show an example of a game where we are able to characterize the necessary neighbor information in order to show that the game has a potential function and the properties of potential games. We consider a network consisting of local access points where the goal of each AP is to allocate power and frequency to achieve some SINR requirement. We use the physical SINR model to validate a successful allocation, and show that given a suitable payoff function the game emits a generalized ordinal potential function under the assumption of sufficient neighbor information. Through simulations we evaluate the performance of the proposed game on a large scale in relation to the amount of information at each AP.

Includes members:
  Member "large scale distributed internet based discovery mechanism for dynamic spectrum allocation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed discovery clients for spectrum allocation")
  Member "the domino effect in decentralized wireless networks":
    also connected through other hyperedges:
      (none)
  Member "adaptive channel allocation spectrum etiquette for cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges")

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.NI(Networking and Internet Architecture)