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 "detection under one bit messaging over adaptive networks"):
  Abstract: This work studies the operation of multi-agent networks engaged in binary decision tasks, and derives performance expressions and performance operating curves under challenging conditions with some revealing insights. One of the main challenges in the analysis is that agents are only allowed to exchange one-bit messages, and the information at each agent therefore consists of both continuous and discrete components. Due to this mixed nature, the steady-state distribution of the state of each agent cannot be inferred from direct application of central limit arguments. Instead, the behavior of the continuous component is characterized in integral form by using a log-characteristic function, while the behavior of the discrete component is characterized by means of an asymmetric Bernoulli convolution. By exploiting these results, the article derives reliable approximate performance expressions for the network nodes that match well with the simulated results for a wide range of system parameters. The results also reveal an important interplay between continuous adaptation under constant step-size learning and the binary nature of the messages exchanged with neighbors.

Includes members:
  Member "quantized consensus by the admm probabilistic versus deterministic quantizers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed detection in ad hoc networks through quantized consensus")
  Member "distributed detection over adaptive networks refined asymptotics and the role of connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inverse graph learning over optimization networks")
  Member "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "inverse graph learning over optimization networks")
  Member "distributed parameter estimation with quantized communication via running average":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "source coding optimization for distributed average consensus")
  Member "on the learning behavior of adaptive networks part ii performance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inverse graph learning over optimization networks")
  Member "active sequential hypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "decentralized sequential active hypothesis testing and the mac feedback capacity")
  Member "on the learning behavior of adaptive networks part i transient analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inverse graph learning over optimization networks")
  Member "distributed detection over noisy networks large deviations analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed learning with adversarial agents under relaxed network condition")

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)