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: "belief consensus algorithms for fast distributed target tracking in wireless sensor networks"
  Abstract: In distributed target tracking for wireless sensor networks, agreement on the target state can be achieved by the construction and maintenance of a communication path, in order to exchange information regarding local likelihood functions. Such an approach lacks robustness to failures and is not easily applicable to ad-hoc networks. To address this, several methods have been proposed that allow agreement on the global likelihood through fully distributed belief consensus (BC) algorithms, operating on local likelihoods in distributed particle filtering (DPF). However, a unified comparison of the convergence speed and communication cost has not been performed. In this paper, we provide such a comparison and propose a novel BC algorithm based on belief propagation (BP). According to our study, DPF based on metropolis belief consensus (MBC) is the fastest in loopy graphs, while DPF based on BP consensus is the fastest in tree graphs. Moreover, we found that BC-based DPF methods have lower communication overhead than data flooding when the network is sufficiently sparse.

Connected through hyperedge HE-1 (induced by source paper "cooperative network node positioning techniques using underwater radio communications"):
  with members:
    - "simulation of underwater rf wireless sensor networks using castalia"
    - "decomposition by partial linearization parallel optimization of multi agent systems"

Connected through hyperedge HE-2 (induced by source paper "simulation of underwater rf wireless sensor networks using castalia"):
  with members:
    - "cooperative network node positioning techniques using underwater radio communications"

Connected through hyperedge HE-3 (induced by source paper "cooperative simultaneous localization and tracking in mobile agent networks"):
  with members:
    - "approximate evaluation of marginal association probabilities with belief propagation"
    - "cooperative synchronization in wireless networks"
    - "sigma point belief propagation"
    - "simultaneous distributed sensor self localization and target tracking using belief propagation and likelihood consensus"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-4 (induced by source paper "distributed localization and tracking of mobile networks including noncooperative objects extended version"):
  with members:
    - "approximate evaluation of marginal association probabilities with belief propagation"
    - "cooperative synchronization in wireless networks"
    - "sigma point belief propagation"
    - "simultaneous distributed sensor self localization and target tracking using belief propagation and likelihood consensus"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-5 (induced by source paper "distributed estimation with information seeking control in agent network"):
  with members:
    - "cooperative simultaneous localization and tracking in mobile agent networks"
    - "cooperative synchronization in wireless networks"
    - "simultaneous distributed sensor self localization and target tracking using belief propagation and likelihood consensus"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-6 (induced by source paper "simultaneous distributed sensor self localization and target tracking using belief propagation and likelihood consensus"):
  with members:
    - "gossip algorithms for distributed signal processing"

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)