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 power allocation with sinr constraints using trial and error learning"):
  Abstract: In this paper, we address the problem of global transmit power minimization in a self-congiguring network where radio devices are subject to operate at a minimum signal to interference plus noise ratio (SINR) level. We model the network as a parallel Gaussian interference channel and we introduce a fully decentralized algorithm (based on trial and error) able to statistically achieve a congiguration where the performance demands are met. Contrary to existing solutions, our algorithm requires only local information and can learn stable and efficient working points by using only one bit feedback. We model the network under two different game theoretical frameworks: normal form and satisfaction form. We show that the converging points correspond to equilibrium points, namely Nash and satisfaction equilibrium. Similarly, we provide sufficient conditions for the algorithm to converge in both formulations. Moreover, we provide analytical results to estimate the algorithm's performance, as a function of the network parameters. Finally, numerical results are provided to validate our theoretical conclusions. Keywords: Learning, power control, trial and error, Nash equilibrium, spectrum sharing.

Includes members:
  Member "quality of service provisioning in decentralized networks a satisfaction equilibrium approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks")
  Member "on the nash equilibria in decentralized parallel interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "game theoretic dynamic channel allocation for frequency selective interference channels")
  Member "learning equilibria with partial information in decentralized wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interference coordination via power domain channel estimation")
  Member "on the benefits of bandwidth limiting in decentralized vector multiple access channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the nash equilibria in decentralized parallel interference channels")
  Member "distributed power allocation with rate constraints in gaussian parallel interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "game theoretic dynamic channel allocation for frequency selective interference channels")
  Member "optimal linear precoding strategies for wideband non cooperative systems based on game theory part ii algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mixed monotonic programming for fast global optimization")

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.GT(Computer Science and Game Theory)