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: "multitask diffusion adaptation over networks"
  Abstract: Adaptive networks are suitable for decentralized inference tasks, e.g., to monitor complex natural phenomena. Recent research works have intensively studied distributed optimization problems in the case where the nodes have to estimate a single optimum parameter vector collaboratively. However, there are many important applications that are multitask-oriented in the sense that there are multiple optimum parameter vectors to be inferred simultaneously, in a collaborative manner, over the area covered by the network. In this paper, we employ diffusion strategies to develop distributed algorithms that address multitask problems by minimizing an appropriate mean-square error criterion with $\ell_2$-regularization. The stability and convergence of the algorithm in the mean and in the mean-square sense is analyzed. Simulations are conducted to verify the theoretical findings, and to illustrate how the distributed strategy can be used in several useful applications related to spectral sensing, target localization, and hyperspectral data unmixing.

Connected through hyperedge HE-1 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis"):
  with members:
    - "adaptation and learning over networks under subspace constraints part ii performance analysis"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "diffusion lms for multitask problems with local linear equality constraints"
    - "network lasso clustering and optimization in large graphs"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-2 (induced by source paper "clustered multitask nonnegative matrix factorization for spectral unmixing of hyperspectral data"):
  with members:
    - "sparsity constrained distributed unmixing of hyperspectral data"
    - "hyperspectral unmixing based on clustered multitask networks"
    - "spectral unmixing of hyperspectral imagery using multilayer nmf"
    - "structured sparse method for hyperspectral unmixing"
    - "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"
    - "hyperspectral data unmixing using gnmf method and sparseness constraint"

Connected through hyperedge HE-3 (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"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-4 (induced by source paper "learning over multitask graphs part i stability analysis"):
  with members:
    - "learning over multitask graphs part ii performance analysis"
    - "diffusion lms for multitask problems with local linear equality constraints"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "signals on graphs uncertainty principle and sampling"
    - "network lasso clustering and optimization in large graphs"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "distributed pareto optimization via diffusion strategies"

Connected through hyperedge HE-5 (induced by source paper "learning over multitask graphs part ii performance analysis"):
  with members:
    - "learning over multitask graphs part i stability analysis"
    - "diffusion lms for multitask problems with local linear equality constraints"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "diffusion lms over multitask networks"
    - "network lasso clustering and optimization in large graphs"
    - "learning laplacian matrix in smooth graph signal representations"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "djam distributed jacobi asynchronous method for learning personal models"):
  with members:
    - "personalized and private peer to peer machine learning"
    - "decentralized collaborative learning of personalized models over networks"
    - "network lasso clustering and optimization in large graphs"
    - "linear convergence rate of a class of distributed augmented lagrangian algorithms"

Connected through hyperedge HE-8 (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"
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "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"

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)