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 "fictitious play with inertia learns pure equilibria in distributed games with incomplete information"):
  Abstract: The paper studies a class of learning dynamics, termed inertial best response dynamics, based on the Fictitious Play (FP) algorithm. A general convergence result is established showing that inertial best response dynamics converge, almost surely, to pure-strategy Nash equilibrium (NE). As an application of the general result, the paper considers learning in a setting where (i) players have some uncertainty about the underlying state of the world, and (ii) all inter-agent communication is restricted to a preassigned (possibly sparse) communication graph. The paper studies two particular instances of inertial best response dynamics in this setting: FP with inertia and Joint Strategy FP with inertia. General conditions are established under which the algorithms converge to pure-strategy NE, and a fully distributed variant of each algorithm is presented. Finally, numerical simulations are provided which verify the findings.

Includes members:
  Member "diffusion adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparsity constrained distributed unmixing of hyperspectral data")
  Member "on the structure of weakly acyclic games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coordination games on weighted directed graphs")
  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 "higher dimensional consensus learning in large scale networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cooperative network synchronization asymptotic analysis")
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inverse graph learning over optimization networks")
  Member "methodologies for analyzing equilibria in wireless games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed multi user wireless charging power allocation")

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.SY(Systems and Control)