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: "cooperative synchronization in wireless networks"
  Abstract: Synchronization is a key functionality in wireless networks, enabling a wide variety of services. We consider a Bayesian inference framework whereby network nodes can achieve phase and skew synchronization in a fully distributed way. In particular, under the assumption of Gaussian measurement noise, we derive two message passing methods (belief propagation and mean field), analyze their convergence behavior, and perform a qualitative and quantitative comparison with a number of competing algorithms. We also show that both methods can be applied in networks with and without master nodes. Our performance results are complemented by, and compared with, the relevant Bayesian Cramer-Rao bounds.

Connected through hyperedge HE-1 (induced by source paper "optimizing reweighted belief propagation for likelihood consensus problems"):
  with members:
    - "simultaneous distributed sensor self localization and target tracking using belief propagation and likelihood consensus"

Connected through hyperedge HE-2 (induced by source paper "scalable and passive wireless network clock synchronization in los environments"):
  with members:
    - "joint ranging and clock parameter estimation by wireless round trip time measurements"
    - "experimental demonstration of nanosecond accuracy wireless network synchronization"
    - "an experimental investigation of simo mimo interference alignment ia and coordinated multi point comp"
    - "fundamentals of large sensor networks connectivity capacity clocks and computation"
    - "femtocell networks a survey"

Connected through hyperedge HE-3 (induced by source paper "cooperative simultaneous localization and synchronization in mobile agent networks"):
  with members:
    - "cooperative joint localization and clock synchronization based on gaussian message passing in asynchronous wireless networks"
    - "space time hierarchical graph based cooperative localization in wireless sensor networks"
    - "sigma point belief propagation"
    - "joint localization and clock synchronization for wireless sensor networks"

Connected through hyperedge HE-4 (induced by source paper "cooperative joint localization and clock synchronization based on gaussian message passing in asynchronous wireless networks"):
  with members:
    - "collaborative high accuracy localization in mobile multipath environments"
    - "merging belief propagation and the mean field approximation a free energy approach"
    - "operations for learning with graphical models"

Connected through hyperedge HE-5 (induced by source paper "joint ranging and clock parameter estimation by wireless round trip time measurements"):
  with members:
    - "joint localization and clock synchronization for wireless sensor networks"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-6 (induced by source paper "cooperative simultaneous localization and tracking in mobile agent networks"):
  with members:
    - "approximate evaluation of marginal association probabilities with belief propagation"
    - "sigma point belief propagation"
    - "belief consensus algorithms for fast distributed target tracking in wireless sensor 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-7 (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"
    - "sigma point belief propagation"
    - "belief consensus algorithms for fast distributed target tracking in wireless sensor 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-8 (induced by source paper "distributed estimation with information seeking control in agent network"):
  with members:
    - "cooperative simultaneous localization and tracking in mobile agent networks"
    - "belief consensus algorithms for fast distributed target tracking in wireless sensor networks"
    - "simultaneous distributed sensor self localization and target tracking using belief propagation and likelihood consensus"
    - "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)