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 "robust federated learning in a heterogeneous environment"):
  Abstract: We study a recently proposed large-scale distributed learning paradigm, namely Federated Learning, where the worker machines are end users' own devices. Statistical and computational challenges arise in Federated Learning particularly in the presence of heterogeneous data distribution (i.e., data points on different devices belong to different distributions signifying different clusters) and Byzantine machines (i.e., machines that may behave abnormally, or even exhibit arbitrary and potentially adversarial behavior). To address the aforementioned challenges, first we propose a general statistical model for this problem which takes both the cluster structure of the users and the Byzantine machines into account. Then, leveraging the statistical model, we solve the robust heterogeneous Federated Learning problem \emph{optimally}; in particular our algorithm matches the lower bound on the estimation error in dimension and the number of data points. Furthermore, as a by-product, we prove statistical guarantees for an outlier-robust clustering algorithm, which can be considered as the Lloyd algorithm with robust estimation. Finally, we show via synthetic as well as real data experiments that the estimation error obtained by our proposed algorithm is significantly better than the non-Byzantine-robust algorithms; in particular, we gain at least by 53\% and 33\% for synthetic and real data experiments, respectively, in typical settings.

Includes members:
  Member "robust and communication efficient federated learning from non iid data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ternary compression for communication efficient federated learning")
  Member "byzantine stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "federated over the air subspace learning from incomplete data")
  Member "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to detect malicious clients for robust federated learning")
  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 "federated learning with non iid data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "trends and advancements in deep neural network communication")
  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 "learning mixtures of sparse linear regressions using sparse graph codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "iterative least trimmed squares for mixed linear regression")

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)