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: "compressive diffusion strategies over distributed networks for reduced communication load"
  Abstract: We study the compressive diffusion strategies over distributed networks based on the diffusion implementation and adaptive extraction of the information from the compressed diffusion data. We demonstrate that one can achieve a comparable performance to the full information exchange configurations, even if the diffused information is compressed into a scalar or a single bit, i.e., a tremendous reduction in the communication load. To this end, we provide a complete performance analysis for the compressive diffusion strategies. We analyze the transient, the steady-state and the tracking performances of the configurations in which the diffused data is compressed into a scalar or a single-bit. We propose a new adaptive combination method improving the convergence performance of the compressive diffusion strategies further. In the new method, we introduce one more freedom-of-dimension in the combination matrix and adapt it by using the conventional mixture approach in order to enhance the convergence performance for any possible combination rule used for the full diffusion configuration. We demonstrate that our theoretical analysis closely follow the ensemble averaged results in our simulations. We provide numerical examples showing the improved convergence performance with the new adaptive combination method while tremendously reducing the communication load.

Connected through hyperedge HE-1 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation"):
  with members:
    - "distributed local linear parameter estimation using gaussian spawn"
    - "robust decentralized detection and social learning in tandem networks"
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "diffusion lms over multitask networks"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "distributed localization of a rf target in nlos environments"
    - "multitask diffusion adaptation over asynchronous networks"
    - "diffusion lms for clustered multitask networks"

Connected through hyperedge HE-2 (induced by source paper "adaptive diffusion schemes for heterogeneous networks"):
  with members:
    - "diffusion lms over multitask networks"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"
    - "on the influence of informed agents on learning and adaptation over networks"
    - "sparse distributed learning based on diffusion adaptation"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-3 (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"
    - "distributed decision making over adaptive networks"
    - "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-4 (induced by source paper "team optimal distributed mmse estimation in general and tree networks"):
  with members:
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-5 (induced by source paper "on the asymptotic bias of the diffusion based distributed pareto optimization"):
  with members:
    - "dictionary learning over distributed models"
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "diffusion lms over multitask networks"
    - "information exchange and learning dynamics over weakly connected adaptive networks"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "analysis of a reduced communication diffusion lms algorithm"
    - "distributed pareto optimization via diffusion strategies"
    - "on the learning behavior of adaptive networks part ii performance analysis"

Connected through hyperedge HE-6 (induced by source paper "decoupled adapt then combine diffusion networks with adaptive combiners"):
  with members:
    - "diffusion lms over multitask networks"
    - "multitask diffusion adaptation over networks"
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion adaptation over networks"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "performance limits for distributed estimation over lms adaptive networks"

Connected through hyperedge HE-7 (induced by source paper "distributed compressed estimation for wireless sensor networks based on compressive sensing"):
  with members:
    - "sparse distributed learning based on diffusion adaptation"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "measurement matrix design for compressive sensing based mimo radar"
    - "mimo radar using compressive sampling"
    - "distributed compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "partial diffusion least mean square estimation over networks under noisy information exchange"):
  with members:
    - "single bit and reduced dimension diffusion strategies over distributed networks"
    - "diffusion adaptation over networks"

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.SY(Systems and Control)