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 learning in wireless sensor networks"
  Abstract: The problem of distributed or decentralized detection and estimation in applications such as wireless sensor networks has often been considered in the framework of parametric models, in which strong assumptions are made about a statistical description of nature. In certain applications, such assumptions are warranted and systems designed from these models show promise. However, in other scenarios, prior knowledge is at best vague and translating such knowledge into a statistical model is undesirable. Applications such as these pave the way for a nonparametric study of distributed detection and estimation. In this paper, we review recent work of the authors in which some elementary models for distributed learning are considered. These models are in the spirit of classical work in nonparametric statistics and are applicable to wireless sensor networks.

Connected through hyperedge HE-1 (induced by source paper "diffusion lms over multitask networks"):
  with members:
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "distributed diffusion based lms for node specific parameter estimation over adaptive networks"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "a sparsity promoting adaptive algorithm for distributed learning"

Connected through hyperedge HE-2 (induced by source paper "machine learning in wireless sensor networks algorithms strategies and applications"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "in network outlier detection in wireless sensor networks"
    - "exact soft confidence weighted learning"
    - "structured compressed sensing from theory to applications"
    - "compressive sensing over networks"

Connected through hyperedge HE-3 (induced by source paper "multitask diffusion adaptation over networks"):
  with members:
    - "distributed decision making over adaptive networks"
    - "diffusion lms for clustered multitask networks"
    - "distributed adaptive networks a graphical evolutionary game theoretic view"
    - "distributed pareto optimization via diffusion strategies"
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "a sparsity promoting adaptive algorithm for distributed learning"

Connected through hyperedge HE-4 (induced by source paper "on the influence of informed agents on learning and adaptation over networks"):
  with members:
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "performance limits for distributed estimation over lms adaptive networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "higher dimensional consensus learning in large scale networks"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-5 (induced by source paper "distributed online big data classification using context information"):
  with members:
    - "efficient optimal learning for contextual bandits"
    - "performance and convergence of multi user online learning"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandits with similarity information"

Connected through hyperedge HE-6 (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"
    - "regression in sensor networks training distributively with alternating projections"

Connected through hyperedge HE-7 (induced by source paper "chebyshev polynomial approximation for distributed signal processing"):
  with members:
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-8 (induced by source paper "collaborative training in sensor networks a graphical model approach"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "distributed kernel regression an algorithm for training collaboratively"

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.IT(Information Theory)