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: "proximity without consensus in online multiagent optimization"
  Abstract: We consider stochastic optimization problems in multiagent settings, where a network of agents aims to learn parameters that are optimal in terms of a global convex objective, while giving preference to locally observed streaming information. To do so, we depart from the canonical decentralized optimization framework where agreement constraints are enforced, and instead formulate a problem where each agent minimizes a global convex objective while enforcing network proximity constraints. This formulation includes online consensus optimization as a special case, but allows for the more general hypothesis that there is data heterogeneity across the network. To solve this problem, we propose using a stochastic saddle point algorithm inspired by Arrow and Hurwicz. This method yields a decentralized algorithm for processing observations sequentially received at each node of the network. Using Lagrange multipliers to penalize the discrepancy between them, only neighboring nodes exchange model information. We establish that under a constant step-size regime the time-average suboptimality and constraint violation are contained in a neighborhood whose radius vanishes with increasing number of iterations. As a consequence, we prove that the time-average primal vectors converge to the optimal objective while satisfying the network proximity constraints. We apply this method to the problem of sequentially estimating a correlated random field in a sensor network, as well as an online source localization problem, both of which demonstrate the empirical validity of the aforementioned convergence results.

Connected through hyperedge HE-1 (induced by source paper "q gadmm quantized group admm for communication efficient decentralized machine learning"):
  with members:
    - "extreme urllc vision challenges and key enablers"
    - "communication efficient massive uav online path control federated learning meets mean field game theory"
    - "predictive control and communication co design a gaussian process regression approach"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "massive autonomous uav path planning a neural network based mean field game theoretic approach"
    - "accelerating federated learning via momentum gradient descent"
    - "advances and open problems in federated learning"
    - "gadmm fast and communication efficient framework for distributed machine learning"

Connected through hyperedge HE-2 (induced by source paper "gadmm fast and communication efficient framework for distributed machine learning"):
  with members:
    - "wireless network intelligence at the edge"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "communication efficient learning of deep networks from decentralized data"
    - "distributed mean estimation with limited communication"
    - "distributed optimization with arbitrary local solvers"
    - "multi agent distributed optimization via inexact consensus admm"
    - "communication efficient distributed dual coordinate ascent"

Connected through hyperedge HE-3 (induced by source paper "secure distributed on device learning networks with byzantine adversaries"):
  with members:
    - "supervised learning under distributed features"
    - "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"
    - "federated multi task learning"
    - "distributed optimization under adversarial nodes"
    - "federated optimization distributed optimization beyond the datacenter"

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)