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: "matrix representation of iterative approximate byzantine consensus in directed graphs"
  Abstract: Abstract : This paper presents a proof of correctness of an iterative approximate Byzantine consensus (IABC) algorithm for directed graphs. The iterative algorithm allows fault- free nodes to reach approximate consensus despite the presence of up to f Byzantine faults. Necessary conditions on the underlying network graph for the existence of a correct IABC algorithm were shown in our recent work [15, 16]. [15] also analyzed a specific IABC algorithm and showed that it performs correctly in any network graph that satisfies the necessary condition, proving that the necessary condition is also sufficient. In this paper, we present an alternate proof of correctness of the IABC algorithm using a familiar technique based on transition matrices [9, 3, 17, 19]. The key contribution of this paper is to exploit the following observation: for a given evolution of the state vector corresponding to the state of the fault-free nodes many alternate state transition matrices may be chosen to model that evolution correctly. For a given state evolution, we identify one approach to suitably design the transition matrices so that the standard tools for proving convergence can be applied to the Byzantine fault-tolerant algorithm as well. In particular, the transition matrix for each iteration is designed such that each row of the matrix contains a large enough number of elements that are bounded away from 0.

Connected through hyperedge HE-1 (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"
    - "defending non bayesian learning against adversarial attacks"
    - "distributed optimization under adversarial nodes"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-2 (induced by source paper "resilient consensus for multi agent systems subject to differential privacy requirements"):
  with members:
    - "resilient consensus of second order agent networks asynchronous update rules with delays"
    - "resilient randomized quantized consensus"
    - "r robustness and r s robustness of circulant graphs"
    - "exploiting nodes symmetries to control synchronization and consensus patterns in multiagent systems"
    - "a jamming resilient algorithm for self triggered network coordination"
    - "reaching approximate byzantine consensus with multi hop communication"
    - "differentially private distributed optimization"
    - "differentially private iterative synchronous consensus"

Connected through hyperedge HE-3 (induced by source paper "finite time guarantees for byzantine resilient distributed state estimation with noisy measurements"):
  with members:
    - "byzantine resilient distributed observers for lti systems"
    - "byrdie byzantine resilient distributed coordinate descent for decentralized learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "byzantine vector consensus in complete graphs"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-4 (induced by source paper "byrdie byzantine resilient distributed coordinate descent for decentralized learning"):
  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"
    - "defending against saddle point attack in byzantine robust distributed learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "cloud k svd a collaborative dictionary learning algorithm for big distributed data"
    - "fault tolerant distributed optimization part iv constrained optimization with arbitrary directed networks"

Connected through hyperedge HE-5 (induced by source paper "defending non bayesian learning against adversarial attacks"):
  with members:
    - "learning without recall a case for log linear learning"
    - "finite time analysis of the distributed detection problem"
    - "reaching approximate byzantine consensus with multi hop communication"
    - "byzantine vector consensus in complete graphs"
    - "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"
    - "consensus of multi agent networks in the presence of adversaries using only local information"

Connected through hyperedge HE-6 (induced by source paper "distributed optimization under adversarial nodes"):
  with members:
    - "byzantine multi agent optimization part ii"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-7 (induced by source paper "fault tolerant distributed optimization part iv constrained optimization with arbitrary directed networks"):
  with members:
    - "fault tolerant multi agent optimization part iii"
    - "byzantine multi agent optimization part ii"
    - "approximate consensus in highly dynamic networks the role of averaging algorithms"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-8 (induced by source paper "fault tolerant multi agent optimization part iii"):
  with members:
    - "data falsification attacks on consensus based detection systems"
    - "byzantine multi agent optimization part ii"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

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)