human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "energy balancing for robotic aided clustered wireless sensor networks using mobility diversity algorithms"):
  with members:
    - "optimal power allocation for distributed blue estimation with linear spatial collaboration"

Connected through hyperedge HE-2 (induced by source paper "distributed adaptive learning of graph signals"):
  with members:
    - "multitask diffusion adaptation over networks with common latent representations"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "adaptive least mean squares estimation of graph signals"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "subsampling for graph power spectrum estimation"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-3 (induced by source paper "adaptive least mean squares estimation of graph signals"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "toward an uncertainty principle for weighted graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "on the degrees of freedom of signals on graphs"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "a distributed tracking algorithm for reconstruction of graph signals"

Connected through hyperedge HE-4 (induced by source paper "least mean squares estimation of graph signals"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "toward an uncertainty principle for weighted graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "on the degrees of freedom of signals on graphs"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "a distributed tracking algorithm for reconstruction of graph signals"
    - "signal recovery on graphs variation minimization"

Connected through hyperedge HE-5 (induced by source paper "next in network nonconvex optimization"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "hybrid random deterministic parallel algorithms for nonconvex big data optimization"
    - "parallel selective algorithms for big data optimization"
    - "distributed compressed sensing for static and time varying networks"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"
    - "distributed maximum likelihood sensor network localization"
    - "sparse distributed learning based on diffusion adaptation"

Connected through hyperedge HE-6 (induced by source paper "distributed detection over adaptive networks refined asymptotics and the role of connectivity"):
  with members:
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "asymptotic performance of adaptive distributed detection over networks"
    - "distributed detection over noisy networks large deviations analysis"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-7 (induced by source paper "distributed inference over directed networks performance limits and optimal design"):
  with members:
    - "distributed kalman filtering over massive data sets analysis through large deviations of random riccati equations"
    - "asymptotic performance of adaptive distributed detection over networks"
    - "sparse distributed learning based on diffusion adaptation"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-8 (induced by source paper "dictionary learning over distributed models"):
  with members:
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "distributed pareto optimization via diffusion strategies"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Question: Please tell me which class the center node 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)