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 "cross layer distributed power control a repeated game formulation to improve the sum energy efficiency"):
  Abstract: The main objective of this work is to improve the energy efficiency (EE) of a multiple access channel (MAC) system, through power control, in a distributed manner. In contrast with many existing works on energy-efficient power control, which ignore the possible presence of a queue at the transmitter, we consider a new generalized cross-layer EE metric. This approach is relevant when the transmitters have a non-zero energy cost even when the radiated power is zero and takes into account the presence of a finite packet buffer and packet arrival at the transmitter. As the Nash equilibrium (NE) is an energy-inefficient solution, the present work aims at overcoming this deficit by improving the global energy efficiency. Indeed, as the considered system has multiple agencies each with their own interest, the performance metric reflecting the individual interest of each decision-maker is the global energy efficiency defined then as the sum over individual energy efficiencies. Repeated games (RG) are investigated through the study of two dynamic games (finite RG and discounted RG), whose equilibrium is defined when introducing a new operating point (OP), Pareto-dominating the NE and relying only on individual channel state information (CSI). Accordingly, closed-form expressions of the minimum number of stages of the game for finite RG (FRG) and the maximum discount factor of the discounted RG (DRG) were established. Our contributions consist of improving the system performances in terms of powers and utilities when using the new OP compared to the NE and the Nash bargaining (NB) solution. Moreover, the cross-layer model in the RG formulation leads to achieving a shorter minimum number of stages in the FRG even for higher number of users. In addition, the social welfare (sum of utilities) in the DRG decreases slightly with the cross-layer model when the number of users increases while it is reduced considerably with the Goodman model. Finally, we show that in real systems with random packet arrivals, the cross-layer power control algorithm outperforms the Goodman algorithm.

Includes members:
  Member "a cross layer approach for distributed energy efficient power control in interference networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "game theory for multi access edge computing survey use cases and future trends")
  Member "cross layer design for green power control":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a cross layer approach for distributed energy efficient power control in interference networks")
  Member "framework for link level energy efficiency optimization with informed transmitter":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed power control with partial channel state information performance characterization and design")
  Member "energy efficient precoding for multiple antenna terminals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "decision set optimization and energy efficient mimo communications")
  Member "methodologies for analyzing equilibria in wireless games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed multi user wireless charging power allocation")
  Member "introducing hierarchy in energy games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed power control with partial channel state information performance characterization and design")
  Member "energy efficient communications in cdma networks a game theoretic analysis considering operating costs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed power control with partial channel state information performance characterization and design")

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)