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 decision making over adaptive networks"
  Abstract: In distributed processing, agents generally collect data generated by the same underlying unknown model (represented by a vector of parameters) and then solve an estimation or inference task cooperatively. In this paper, we consider the situation in which the data observed by the agents may have risen from two different models. Agents do not know beforehand which model accounts for their data and the data of their neighbors. The objective for the network is for all agents to reach agreement on which model to track and to estimate this model cooperatively. In these situations, where agents are subject to data from unknown different sources, conventional distributed estimation strategies would lead to biased estimates relative to any of the underlying models. We first show how to modify existing strategies to guarantee unbiasedness. We then develop a classification scheme for the agents to identify the models that generated the data, and propose a procedure by which the entire network can be made to converge towards the same model through a collaborative decision-making process. The resulting algorithm is applied to model fish foraging behavior in the presence of two food sources.

Connected through hyperedge HE-1 (induced by source paper "diffusion multi rate lms algorithm for acoustic sensor networks"):
  with members:
    - "diffusion leaky lms algorithm analysis and implementation"

Connected through hyperedge HE-2 (induced by source paper "diffusion leaky lms algorithm analysis and implementation"):
  with members:
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "diffusion adaptation over multi agent networks with wireless link impairments"
    - "a novel normalized sign algorithm for system identification under impulsive noise interference"
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "analysis of a reduced communication diffusion lms algorithm"
    - "sparse distributed learning based on diffusion adaptation"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-3 (induced by source paper "diffusion lms strategies in sensor networks with noisy input data"):
  with members:
    - "sparse distributed learning based on diffusion adaptation"
    - "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"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-4 (induced by source paper "a class of diffusion algorithms with logarithmic cost over adaptive sparse volterra network"):
  with members:
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "a novel family of adaptive filtering algorithms based on the logarithmic cost"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"
    - "performance limits for distributed estimation over lms adaptive networks"

Connected through hyperedge HE-5 (induced by source paper "multitask diffusion adaptation over networks"):
  with members:
    - "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"
    - "distributed consensus algorithms in sensor networks link failures and channel noise"

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)