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: "defending non bayesian learning against adversarial attacks"
  Abstract: This paper addresses the problem of non-Bayesian learning over multi-agent networks, where agents repeatedly collect partially informative observations about an unknown state of the world, and try to collaboratively learn the true state. We focus on the impact of the adversarial agents on the performance of consensus-based non-Bayesian learning, where non-faulty agents combine local learning updates with consensus primitives. In particular, we consider the scenario where an unknown subset of agents suffer Byzantine faults -- agents suffering Byzantine faults behave arbitrarily. Two different learning rules are proposed.

Connected through hyperedge HE-1 (induced by source paper "fall of empires breaking byzantine tolerant sgd by inner product manipulation"):
  with members:
    - "byzantine stochastic gradient descent"
    - "analyzing federated learning through an adversarial lens"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "how to backdoor federated learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-2 (induced by source paper "a new approach for distributed hypothesis testing with extensions to byzantine resilience"):
  with members:
    - "byzantine resilient distributed observers for lti systems"
    - "distributed observers for lti systems"
    - "distributed optimization under adversarial nodes"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "matrix representation of iterative approximate byzantine consensus in directed graphs"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-3 (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"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "asynchronous stochastic gradient descent with delay compensation"

Connected through hyperedge HE-4 (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"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "asynchronous stochastic gradient descent with delay compensation"

Connected through hyperedge HE-5 (induced by source paper "non bayesian social learning with uncertain models"):
  with members:
    - "social learning over weakly connected graphs"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-6 (induced by source paper "distributed learning with adversarial agents under relaxed network condition"):
  with members:
    - "iterative byzantine vector consensus in incomplete graphs"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "distributed detection over noisy networks large deviations analysis"

Connected through hyperedge HE-7 (induced by source paper "byzantine stochastic gradient descent"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "generalized byzantine tolerant sgd"
    - "efficient algorithms for outlier robust regression"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "how to make the gradients small stochastically"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "learning from untrusted data"

Connected through hyperedge HE-8 (induced by source paper "zeno byzantine suspicious stochastic gradient descent"):
  with members:
    - "byzantine 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"
    - "federated optimization distributed machine learning for on device intelligence"
    - "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)