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: "coordinate descent diffusion learning by networked agents"
  Abstract: This paper examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme. In this setting, the adaptation step by each agent is limited to a random subset of the coordinates of its stochastic gradient vector. The selection of coordinates varies randomly from iteration to iteration and from agent to agent across the network. Such schemes are useful in reducing computational complexity at each iteration in power-intensive large data applications. They are also useful in modeling situations where some partial gradient information may be missing at random. Interestingly, the results show that the steady-state performance of the learning strategy is not always degraded, while the convergence rate suffers some degradation. The results provide yet another indication of the resilience and robustness of adaptive distributed strategies.

Connected through hyperedge HE-1 (induced by source paper "supervised learning under distributed features"):
  with members:
    - "variance reduced stochastic learning by networked agents under random reshuffling"
    - "variance reduced stochastic learning under random reshuffling"
    - "distributed learning for stochastic generalized nash equilibrium problems"
    - "sdca without duality regularization and individual convexity"
    - "doubly random parallel stochastic methods for large scale learning"
    - "next in network nonconvex optimization"
    - "dictionary learning over distributed models"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-2 (induced by source paper "dynamic average diffusion with randomized coordinate updates"):
  with members:
    - "supervised learning under distributed features"
    - "stochastic gradient push for distributed deep learning"
    - "distributed big data optimization via block iterative convexification and averaging"
    - "next in network nonconvex optimization"
    - "dictionary learning over distributed models"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "distributed pareto optimization via diffusion strategies"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-3 (induced by source paper "distributed big data optimization via block iterative convexification and averaging"):
  with members:
    - "a randomized primal distributed algorithm for partitioned and big data non convex optimization"
    - "distributed nonconvex multiagent optimization over time varying networks"
    - "doubly random parallel stochastic methods for large scale learning"
    - "next in network nonconvex optimization"

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)