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: "byrdie byzantine resilient distributed coordinate descent for decentralized learning"
  Abstract: Distributed machine learning algorithms enable processing of datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfunctional indeed happen in the real world. This paper focuses on the problem of Byzantine failures, which are the hardest to safeguard against in distributed algorithms. While Byzantine fault tolerance has a rich history, existing work does not translate into efficient and practical algorithms for high-dimensional distributed learning tasks. In this paper, two variants of an algorithm termed Byzantine-resilient distributed coordinate descent (ByRDiE) are developed and analyzed that solve distributed learning problems in the presence of Byzantine failures. Theoretical analysis as well as numerical experiments presented in the paper highlight the usefulness of ByRDiE for high-dimensional distributed learning in the presence of Byzantine failures.

Connected through hyperedge HE-1 (induced by source paper "towards byzantine resilient learning in decentralized systems"):
  with members:
    - "zeno robust fully asynchronous sgd"
    - "slsgd secure and efficient distributed on device machine learning"
    - "towards federated learning at scale system design"
    - "e2 train training state of the art cnns with over 80 energy savings"
    - "zeno robust asynchronous sgd with arbitrary number of byzantine workers"
    - "local model poisoning attacks to byzantine robust federated learning"
    - "a little is enough circumventing defenses for distributed learning"
    - "communication compression for decentralized training"

Connected through hyperedge HE-2 (induced by source paper "distributed momentum for byzantine resilient learning"):
  with members:
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "can you really backdoor federated learning"
    - "sgd decentralized byzantine resilience"
    - "momentum based variance reduction in non convex sgd"
    - "a little is enough circumventing defenses for distributed learning"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "generalized byzantine tolerant sgd"

Connected through hyperedge HE-3 (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"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "federated optimization distributed optimization beyond the datacenter"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-4 (induced by source paper "byzantine fault tolerant distributed linear regression"):
  with members:
    - "secure state estimation with byzantine sensors a probabilistic approach"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "byzantine resilient distributed observers for lti systems"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "finite time guarantees for byzantine resilient distributed state estimation with noisy measurements"

Connected through hyperedge HE-5 (induced by source paper "the convergence of stochastic gradient descent in asynchronous shared memory"):
  with members:
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "byzantine tolerant machine learning"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "deep residual learning for image recognition"
    - "staleness aware async sgd for distributed deep learning"
    - "taming the wild a unified analysis of hogwild style algorithms"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"

Connected through hyperedge HE-6 (induced by source paper "finite time guarantees for byzantine resilient distributed state estimation with noisy measurements"):
  with members:
    - "byzantine resilient distributed observers for lti systems"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "byzantine vector consensus in complete graphs"
    - "matrix representation of iterative approximate byzantine consensus in directed graphs"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-7 (induced by source paper "the internet of things secure distributed inference"):
  with members:
    - "byzantine resilient locally optimum detection using collaborative autonomous networks"
    - "data falsification attacks on consensus based detection systems"
    - "distributed bayesian detection in the presence of byzantine data"

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.LG(Machine Learning)