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: "multi agent distributed optimization via inexact consensus admm"
  Abstract: Multi-agent distributed consensus optimization problems arise in many signal processing applications. Recently, the alternating direction method of multipliers (ADMM) has been used for solving this family of problems. ADMM based distributed optimization method is shown to have faster convergence rate compared with classic methods based on consensus subgradient, but can be computationally expensive, especially for problems with complicated structures or large dimensions. In this paper, we propose low-complexity algorithms that can reduce the overall computational cost of consensus ADMM by an order of magnitude for certain large-scale problems. Central to the proposed algorithms is the use of an inexact step for each ADMM update, which enables the agents to perform cheap computation at each iteration. Our convergence analyses show that the proposed methods converge well under some convexity assumptions. Numerical results show that the proposed algorithms offer considerably lower computational complexity than the standard ADMM based distributed optimization methods.

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"
    - "supervised learning under distributed features"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "distributed learning in the non convex world from batch to streaming data and beyond"):
  with members:
    - "distributed learning in non convex environments part ii polynomial escape from saddle points"
    - "decentralized stochastic gradient tracking for empirical risk minimization"
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "stochastic gradient push for distributed deep learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "byrdie byzantine resilient distributed coordinate descent for decentralized learning"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "federated optimization distributed optimization beyond the datacenter"

Connected through hyperedge HE-3 (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-4 (induced by source paper "supervised learning under distributed features"):
  with members:
    - "coordinate descent diffusion learning by networked agents"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "robust and communication efficient collaborative learning"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "an exact quantized decentralized gradient descent algorithm"
    - "speeding up distributed machine learning using codes"
    - "quantized decentralized consensus optimization"
    - "signsgd compressed optimisation for non convex problems"
    - "compressed distributed gradient descent communication efficient consensus over networks"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"

Connected through hyperedge HE-6 (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"
    - "proximity without consensus in online multiagent optimization"
    - "communication efficient learning of deep networks from decentralized data"
    - "distributed mean estimation with limited communication"
    - "distributed optimization with arbitrary local solvers"
    - "communication efficient distributed dual coordinate ascent"

Connected through hyperedge HE-7 (induced by source paper "distributed big data optimization via block communications"):
  with members:
    - "distributed big data optimization via block iterative convexification and averaging"
    - "a randomized primal distributed algorithm for partitioned and big data non convex optimization"
    - "doubly random parallel stochastic methods for large scale learning"
    - "next in network nonconvex optimization"
    - "a fast distributed proximal gradient method"

Connected through hyperedge HE-8 (induced by source paper "networked parallel algorithms for robust convex optimization via the scenario approach"):
  with members:
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "explicit convergence rate of a distributed alternating direction method of multipliers"
    - "robust regression and lasso"

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)