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: "proximal multitask learning over networks with sparsity inducing coregularization"
  Abstract: In this work, we consider multitask learning problems where clusters of nodes are interested in estimating their own parameter vector. Cooperation among clusters is beneficial when the optimal models of adjacent clusters have a good number of similar entries. We propose a fully distributed algorithm for solving this problem. The approach relies on minimizing a global mean-square error criterion regularized by nondifferentiable terms to promote cooperation among neighboring clusters. A general diffusion forward–backward splitting strategy is introduced. Then, it is specialized to the case of sparsity promoting regularizers. A closed-form expression for the proximal operator of a weighted sum of   $\ell _1$  -norms is derived to achieve higher efficiency. We also provide conditions on the step-sizes that ensure convergence of the algorithm in the mean and mean-square error sense. Simulations are conducted to illustrate the effectiveness of the strategy.

Connected through hyperedge HE-1 (induced by source paper "distributed learning in non convex environments part ii polynomial escape from saddle points"):
  with members:
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "distributed stochastic optimization with gradient tracking over strongly connected networks"
    - "escaping saddles with stochastic gradients"
    - "how to escape saddle points efficiently"
    - "next in network nonconvex optimization"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"

Connected through hyperedge HE-2 (induced by source paper "decentralized multi task learning based on extreme learning machines"):
  with members:
    - "on better exploring and exploiting task relationships in multitask learning joint model and feature learning"
    - "federated multi task learning"
    - "distributed multi task relationship learning"
    - "distributed multi task learning with shared representation"
    - "a convex formulation for learning task relationships in multi task learning"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-3 (induced by source paper "adaptation and learning over networks under subspace constraints part ii performance analysis"):
  with members:
    - "adaptation and learning over networks under subspace constraints part i stability analysis"
    - "diffusion lms over multitask networks"
    - "distributed pareto optimization via diffusion strategies"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"

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"
    - "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"
    - "multitask diffusion adaptation over networks"
    - "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"
    - "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"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-6 (induced by source paper "multitask diffusion adaptation over networks with common latent representations"):
  with members:
    - "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"
    - "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"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-7 (induced by source paper "diffusion lms for multitask problems with local linear equality constraints"):
  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"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "multitask diffusion adaptation over asynchronous networks"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "on the mean square performance of the constrained lms algorithm"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-8 (induced by source paper "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"):
  with members:
    - "diffusion lms over multitask networks"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "distributed diffusion based lms for node specific parameter estimation over adaptive networks"
    - "multitask diffusion adaptation over networks"
    - "diffusion adaptation over networks"
    - "gossip algorithms for distributed signal processing"

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)