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: "zeno byzantine suspicious stochastic gradient descent"
  Abstract: We propose Zeno, a new robust aggregation rule, for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The key idea is to suspect the workers that are potentially malicious, and use a ranking-based preference mechanism. This allows us to generalize beyond past work--in our case, the number of malicious workers can be arbitrarily large, and we use only the weakest assumption on honest workers~(at least one honest worker). We prove the convergence of SGD under these scenarios. Empirical results show that Zeno outperforms existing approaches under various attacks.

Connected through hyperedge HE-1 (induced by source paper "sgd decentralized byzantine resilience"):
  with members:
    - "distributed learning with adversarial agents under relaxed network condition"
    - "byzantine stochastic gradient descent"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "federated learning strategies for improving communication efficiency"
    - "adversarial spheres"
    - "finite time guarantees for byzantine resilient distributed state estimation with noisy measurements"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"

Connected through hyperedge HE-2 (induced by source paper "detox a redundancy based framework for faster and more robust gradient aggregation"):
  with members:
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "fast and secure distributed learning in high dimension"
    - "sgd decentralized byzantine resilience"
    - "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"
    - "byzantine robust distributed learning towards optimal statistical rates"

Connected through hyperedge HE-3 (induced by source paper "fast machine learning with byzantine workers and servers"):
  with members:
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "communication efficient distributed sgd with sketching"
    - "sgd decentralized byzantine resilience"
    - "a little is enough circumventing defenses for distributed learning"
    - "generalized byzantine tolerant sgd"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"

Connected through hyperedge HE-4 (induced by source paper "zeno robust fully asynchronous sgd"):
  with members:
    - "byzantine stochastic gradient descent"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "defending non bayesian learning against adversarial attacks"

Connected through hyperedge HE-5 (induced by source paper "byzantine resilient stochastic gradient descent for distributed learning a lipschitz inspired coordinate wise median approach"):
  with members:
    - "byzantine stochastic gradient descent"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "zeno robust asynchronous sgd with arbitrary number of byzantine workers"):
  with members:
    - "byzantine stochastic gradient descent"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "defending non bayesian learning against adversarial attacks"

Connected through hyperedge HE-7 (induced by source paper "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"):
  with members:
    - "byzantine stochastic gradient descent"
    - "securing distributed machine learning in high dimensions"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "defending against saddle point attack in byzantine robust distributed learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "federated multi task learning"

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)