human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"):
  Abstract: In this paper, we propose a class of robust stochastic subgradient methods for distributed learning from heterogeneous datasets at presence of an unknown number of Byzantine workers. The Byzantine workers, during the learning process, may send arbitrary incorrect messages to the master due to data corruptions, communication failures or malicious attacks, and consequently bias the learned model. The key to the proposed methods is a regularization term incorporated with the objective function so as to robustify the learning task and mitigate the negative effects of Byzantine attacks. The resultant subgradient-based algorithms are termed Byzantine-Robust Stochastic Aggregation methods, justifying our acronym RSA used henceforth. In contrast to most of the existing algorithms, RSA does not rely on the assumption that the data are independent and identically distributed (i.i.d.) on the workers, and hence fits for a wider class of applications. Theoretically, we show that: i) RSA converges to a near-optimal solution with the learning error dependent on the number of Byzantine workers; ii) the convergence rate of RSA under Byzantine attacks is the same as that of the stochastic gradient descent method, which is free of Byzantine attacks. Numerically, experiments on real dataset corroborate the competitive performance of RSA and a complexity reduction compared to the state-of-the-art alternatives.

Includes members:
  Member "byzantine stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "federated over the air subspace learning from incomplete data")
  Member "zeno byzantine suspicious stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sgd decentralized byzantine resilience")
  Member "securing distributed machine learning in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "threats to federated learning a survey")
  Member "generalized byzantine tolerant sgd":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to detect malicious clients for robust federated learning")
  Member "byzantine robust distributed learning towards optimal statistical rates":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "turbo aggregate breaking the quadratic aggregation barrier in secure federated learning")
  Member "phocas dimensional byzantine resilient stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed momentum for byzantine resilient learning")
  Member "defending against saddle point attack in byzantine robust distributed learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the sample complexity of adversarial multi source pac learning")
  Member "distributed statistical machine learning in adversarial settings byzantine gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "trends and advancements in deep neural network communication")

Question: Please tell me which class the source paper 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)