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 "towards byzantine resilient learning in decentralized systems"):
  Abstract: With the proliferation of IoT and edge computing, decentralized learning is becoming more promising. When designing a distributed learning system, one major challenge to consider is Byzantine Fault Tolerance (BFT). Past works have researched Byzantine-resilient solutions for centralized distributed learning. However, there are currently no satisfactory solutions with strong efficiency and security in decentralized systems. In this paper, we propose a novel algorithm, Mozi, to achieve BFT in decentralized learning systems. Specifically, Mozi provides a uniform Byzantine-resilient aggregation rule for benign nodes to select the useful parameter updates and filter out the malicious ones in each training iteration. It guarantees that each benign node in a decentralized system can train a correct model under very strong Byzantine attacks with an arbitrary number of faulty nodes. We perform the theoretical analysis to prove the uniform convergence of our proposed algorithm. Experimental evaluations demonstrate the high security and efficiency of Mozi compared to all existing solutions.

Includes members:
  Member "zeno robust fully asynchronous sgd":
    also connected through other hyperedges:
      (none)
  Member "slsgd secure and efficient distributed on device machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic sign sgd for federated learning with theoretical guarantees")
  Member "towards federated learning at scale system design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "e2 train training state of the art cnns with over 80 energy savings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "autodnnchip an automated dnn chip predictor and builder for both fpgas and asics")
  Member "zeno robust asynchronous sgd with arbitrary number of byzantine workers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "basgd buffered asynchronous sgd for byzantine learning")
  Member "local model poisoning attacks to byzantine robust federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "turbo aggregate breaking the quadratic aggregation barrier in secure federated learning")
  Member "a little is enough circumventing defenses for distributed learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed momentum for byzantine resilient learning")
  Member "communication compression for decentralized training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic sign sgd for federated learning with theoretical guarantees")

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)