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 detection over adaptive networks refined asymptotics and the role of connectivity"):
  Abstract: We consider distributed detection problems over adaptive networks, where dispersed agents learn continually from streaming data by means of local interactions. The simultaneous requirements of adaptation and cooperation are achieved by employing diffusion algorithms with constant step-size {\mu}. In [1], [2] some main features of adaptive distributed detection were revealed. By resorting to large deviations analysis, it was established that the Type-I and Type-II error probabilities of all agents vanish exponentially as functions of 1/{\mu}, and that all agents share the same Type-I and Type-II error exponents. However, numerical evidences presented in [1], [2] showed that the theory of large deviations does not capture the fundamental impact of network connectivity on performance, and that additional tools and efforts are required to obtain accurate predictions for the error probabilities. This work addresses these open issues and extends the results of [1], [2] in several directions. By conducting a refined asymptotic analysis based on the mathematical framework of exact asymptotics, we arrive at a revealing and powerful understanding of the universal behavior of distributed detection over adaptive networks: as functions of 1/{\mu}, the error (log-)probability curves corresponding to different agents stay nearly-parallel to each other (as already discovered in [1], [2]), however, these curves are ordered following a criterion reflecting the degree of connectivity of each agent. Depending on the combination weights, the more connected an agent is, the lower its error probability curve will be. Interesting and somehow unexpected behaviors emerge, in terms of the interplay between the network topology, the combination weights, and the inference performance. The lesson learned is that connectivity matters.

Includes members:
  Member "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inverse graph learning over optimization networks")
  Member "distributed detection and estimation in wireless sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "energy balancing for robotic aided clustered wireless sensor networks using mobility diversity algorithms")
  Member "asymptotic performance of adaptive distributed detection over networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed inference over directed networks performance limits and optimal design")
  Member "distributed detection over noisy networks large deviations analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed learning with adversarial agents under relaxed network condition")
  Member "large deviations performance of consensus innovations distributed detection with non gaussian observations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "detection under one bit messaging over adaptive networks")
  Member "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "supervised learning under distributed features")
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inverse graph learning over optimization 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.MA(Multiagent Systems)