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 "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.

Includes members:
  Member "learning without recall a case for log linear learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "naive bayesian learning in social networks")
  Member "finite time analysis of the distributed detection problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "asynchronous distributed hypothesis testing in the presence of crash failures")
  Member "reaching approximate byzantine consensus with multi hop communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "determining r and r s robustness of digraphs using mixed integer linear programming")
  Member "byzantine vector consensus in complete graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "determining r and r s robustness of digraphs using mixed integer linear programming")
  Member "iterative byzantine vector consensus in incomplete graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "determining r and r s robustness of digraphs using mixed integer linear programming")
  Member "exponentially fast parameter estimation in networks using distributed dual averaging":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a new approach for distributed hypothesis testing with extensions to byzantine resilience")
  Member "distributed detection over noisy networks large deviations analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed learning with adversarial agents under relaxed network condition")
  Member "consensus of multi agent networks in the presence of adversaries using only local information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "determining r and r s robustness of digraphs using mixed integer linear programming")

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.DC(Distributed, Parallel, and Cluster Computing)