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: "phocas dimensional byzantine resilient stochastic gradient descent"
  Abstract: We propose a novel robust aggregation rule for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The attackers can arbitrarily manipulate the data transferred between the servers and the workers in the parameter server~(PS) architecture. We prove the Byzantine resilience of the proposed aggregation rules. Empirical analysis shows that the proposed techniques outperform current approaches for realistic use cases and Byzantine attack scenarios.

Connected through hyperedge HE-1 (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-2 (induced by source paper "slsgd secure and efficient distributed on device machine learning"):
  with members:
    - "analyzing federated learning through an adversarial lens"
    - "security and privacy issues in deep learning"
    - "federated learning strategies for improving communication efficiency"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "how to backdoor federated learning"
    - "mitigating sybils in federated learning poisoning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"

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"
    - "zeno byzantine suspicious stochastic gradient descent"
    - "generalized byzantine tolerant sgd"
    - "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"
    - "zeno byzantine suspicious stochastic gradient descent"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "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"
    - "zeno byzantine suspicious stochastic gradient descent"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "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"
    - "zeno byzantine suspicious stochastic gradient descent"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "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"
    - "zeno byzantine suspicious stochastic gradient descent"
    - "securing distributed machine learning in high dimensions"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "defending against saddle point attack in byzantine robust distributed learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "federated multi task learning"

Connected through hyperedge HE-8 (induced by source paper "zeno distributed stochastic gradient descent with suspicion based fault tolerance"):
  with members:
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "byzantine stochastic gradient descent"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "defending non bayesian learning against adversarial attacks"
    - "implementation of a practical distributed calculation system with browsers and javascript and application to distributed deep learning"
    - "mlitb machine learning in the browser"

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.DC(Distributed, Parallel, and Cluster Computing)