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: "regression in sensor networks training distributively with alternating projections"
  Abstract: Wireless sensor networks (WSNs) have attracted considerable attention in recent years and motivate a host of new challenges for distributed signal processing.  The problem of distributed or decentralized  estimation  has often been considered in the context of parametric models.  However, the success of parametric methods is limited by the appropriateness of the strong statistical assumptions made by the models.  In this paper, a more flexible  nonparametric  model for distributed regression is considered that is applicable in a variety of WSN applications including field estimation.  Here, starting with the standard regularized kernel least-squares estimator, a message-passing algorithm for distributed estimation in WSNs is derived. The algorithm can be viewed as an instantiation of the  successive orthogonal projection  (SOP) algorithm. Various practical aspects of the algorithm are discussed and  several numerical simulations validate the potential of the approach.

Connected through hyperedge HE-1 (induced by source paper "distributed optimization in wireless sensor networks an island model framework"):
  with members:
    - "robust probabilistic inference in distributed systems"
    - "ockham s razor in memetic computing three stage optimal memetic exploration"
    - "distributed learning in wireless sensor networks"

Connected through hyperedge HE-2 (induced by source paper "distributed kernel regression an algorithm for training collaboratively"):
  with members:
    - "consistency in models for distributed learning under communication constraints"

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.LG(Machine Learning)