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 and estimation in wireless sensor networks"):
  Abstract: Abstract   Wireless sensor networks (WSNs) are becoming more and more a pervasive tool to monitor a wide range of physical phenomena. The opportunities arising from the many potential applications raise a series of technical challenges coupled with implementation constraints, such as energy supply, latency and vulnerability. The need for an efficient design of a WSN requires a strict interplay between the sensing and communication phases. In this article, we provide an overview of various distributed detection and estimation algorithms, incorporating the constraints imposed by the communication channel and the application requirements. We consider both cases where sensing is distributed, but the decision is centralized, and the case where the decision itself is totally decentralized. Specific attention is devoted to achieve globally optimal results through the interaction of nearby nodes only. We show how the topology of the network plays a significant role in the performance of the distributed algorithms, in terms of energy expenditure and latency. Then, we show how to optimize the network topology in order to minimize energy consumption or to match the graph describing the statistical dependencies among the variables observed by the nodes.

Includes members:
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inverse graph learning over optimization networks")
  Member "energy scaling laws for distributed inference in random fusion networks":
    also connected through other hyperedges:
      (none)
  Member "detection of gauss markov random fields with nearest neighbor dependency":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "diffusion adaptation strategies for distributed estimation over gaussian markov random fields")
  Member "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed learning in the non convex world from batch to streaming data and beyond")
  Member "decentralized maximum likelihood estimation for sensor networks composed of nonlinearly coupled dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed learning from interactions in social networks")
  Member "distributed decision through self synchronizing sensor networks in the presence of propagation delays and nonreciprocal channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed inference for relay assisted sensor networks with intermittent measurements over fading channels")

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.DC(Distributed, Parallel, and Cluster Computing)