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: "diffusion adaptation over networks"
  Abstract: Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self-organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with processing and learning abilities. The agents are linked together through a connection topology, and they cooperate with each other through local interactions to solve distributed optimization, estimation, and inference problems in real-time. The continuous diffusion of information across the network enables agents to adapt their performance in relation to streaming data and network conditions; it also results in improved adaptation and learning performance relative to non-cooperative agents. This article provides an overview of diffusion strategies for adaptation and learning over networks. The article is divided into several sections: 1. Motivation; 2. Mean-Square-Error Estimation; 3. Distributed Optimization via Diffusion Strategies; 4. Adaptive Diffusion Strategies; 5. Performance of Steepest-Descent Diffusion Strategies; 6. Performance of Adaptive Diffusion Strategies; 7. Comparing the Performance of Cooperative Strategies; 8. Selecting the Combination Weights; 9. Diffusion with Noisy Information Exchanges; 10. Extensions and Further Considerations; Appendix A: Properties of Kronecker Products; Appendix B: Graph Laplacian and Network Connectivity; Appendix C: Stochastic Matrices; Appendix D: Block Maximum Norm; Appendix E: Comparison with Consensus Strategies; References.

Connected through hyperedge HE-1 (induced by source paper "sparsity constrained distributed unmixing of hyperspectral data"):
  with members:
    - "distributed unmixing of hyperspectral data with sparsity constraint"
    - "sparsity constrained graph regularized nmf for spectral unmixing of hyperspectral data"
    - "spectral unmixing of hyperspectral imagery using multilayer nmf"
    - "multilayer structured nmf for spectral unmixing of hyperspectral images"
    - "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-2 (induced by source paper "partial consensus and conservative fusion of gaussian mixtures for distributed phd fusion"):
  with members:
    - "robust distributed fusion with labeled random finite sets"
    - "distributed smc phd fusion for partial arithmetic average consensus"
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "distributed fusion with multi bernoulli filter based on generalized covariance intersection"
    - "sigma point belief propagation"

Connected through hyperedge HE-3 (induced by source paper "a multi task gradient descent method for multi label learning"):
  with members:
    - "collaboration based multi label learning"
    - "a survey on multi task learning"
    - "multi label learning with global and local label correlation"

Connected through hyperedge HE-4 (induced by source paper "detection under one bit messaging over adaptive networks"):
  with members:
    - "quantized consensus by the admm probabilistic versus deterministic quantizers"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "distributed parameter estimation with quantized communication via running average"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "active sequential hypothesis testing"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "distributed detection over noisy networks large deviations analysis"

Connected through hyperedge HE-5 (induced by source paper "online distributed learning over networks in rkh spaces using random fourier features"):
  with members:
    - "a diffusion kernel lms algorithm for nonlinear adaptive networks"
    - "efficient klms and krls algorithms a random fourier feature perspective"
    - "on the error of random fourier features"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "multitask diffusion adaptation over networks"
    - "extension of wirtinger s calculus to reproducing kernel hilbert spaces and the complex kernel lms"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-6 (induced by source paper "deterministic and randomized diffusion based iterative generalized hard thresholding difight for distributed sparse signal recovery"):
  with members:
    - "next in network nonconvex optimization"
    - "greedy sparsity promoting algorithms for distributed learning"
    - "distributed compressed sensing for static and time varying networks"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "sparse distributed learning based on diffusion adaptation"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-7 (induced by source paper "fade fast and asymptotically efficient distributed estimator for dynamic networks"):
  with members:
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"
    - "fast distributed gradient methods"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-8 (induced by source paper "robust sequential detection in distributed sensor networks"):
  with members:
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "minimax robust hypothesis testing"
    - "multi target tracking in distributed sensor networks using particle phd filters"
    - "theoretical bounds in minimax decentralized hypothesis testing"

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.MA(Multiagent Systems)