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: "supervised learning under distributed features"
  Abstract: This paper studies the problem of learning under both large datasets and large-dimensional feature space scenarios. The feature information is assumed to be spread across agents in a network, where each agent observes some of the features. Through local cooperation, the agents are supposed to interact with each other to solve an inference problem and converge towards the global minimizer of an empirical risk. We study this problem exclusively in the primal domain, and propose new and effective distributed solutions with guaranteed convergence to the minimizer with linear rate under strong convexity. This is achieved by combining a dynamic diffusion construction, a pipeline strategy, and variance-reduced techniques. Simulation results illustrate the conclusions.

Connected through hyperedge HE-1 (induced by source paper "distributed dual coordinate ascent in general tree networks and its application in federated learning"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "energy efficient federated learning over wireless communication networks"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "revisiting distributed synchronous sgd"
    - "distributed asynchronous dual free stochastic dual coordinate ascent"
    - "adding vs averaging in distributed primal dual optimization"
    - "fast distributed asynchronous sgd with variance reduction"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-2 (induced by source paper "secure distributed on device learning networks with byzantine adversaries"):
  with members:
    - "byzantine stochastic gradient descent"
    - "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "proximity without consensus in online multiagent optimization"
    - "federated multi task learning"
    - "distributed optimization under adversarial nodes"
    - "federated optimization distributed optimization beyond the datacenter"

Connected through hyperedge HE-3 (induced by source paper "network classifiers with output smoothing"):
  with members:
    - "a distributed method for fitting laplacian regularized stratified models"
    - "graph embedding techniques applications and performance a survey"
    - "representation learning on graphs methods and applications"
    - "a sampling theory perspective of graph based semi supervised learning"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-4 (induced by source paper "learning privately over distributed features an admm sharing approach"):
  with members:
    - "dp admm admm based distributed learning with differential privacy"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "learning privately from multiparty data"
    - "privacy via the johnson lindenstrauss transform"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-5 (induced by source paper "dynamic average diffusion with randomized coordinate updates"):
  with members:
    - "stochastic gradient push for distributed deep learning"
    - "coordinate descent diffusion learning by networked agents"
    - "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"

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)