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: "iterative byzantine vector consensus in incomplete graphs"
  Abstract: This work addresses Byzantine vector consensus (BVC), wherein the input at each process is a d-dimensional vector of reals, and each process is expected to decide on a decision vector that is in the convex hull of the input vectors at the fault-free processes [3, 8]. The input vector at each process may also be viewed as a point in the d-dimensional Euclidean space R^d, where d > 0 is a finite integer. Recent work [3, 8] has addressed Byzantine vector consensus in systems that can be modeled by a complete graph. This paper considers Byzantine vector consensus in incomplete graphs. In particular, we address a particular class of iterative algorithms in incomplete graphs, and prove a necessary condition, and a sufficient condition, for the graphs to be able to solve the vector consensus problem iteratively. We present an iterative Byzantine vector consensus algorithm, and prove it correct under the sufficient condition. The necessary condition presented in this paper for vector consensus does not match with the sufficient condition for d > 1; thus, a weaker condition may potentially suffice for Byzantine vector consensus.

Connected through hyperedge HE-1 (induced by source paper "determining r and r s robustness of digraphs using mixed integer linear programming"):
  with members:
    - "determining r robustness of arbitrary digraphs using zero one linear integer programming"
    - "secure distributed state estimation of an lti system over time varying networks and analog erasure channels"
    - "resilient consensus of second order agent networks asynchronous update rules with delays"
    - "on connectivity and robustness in random intersection graphs"
    - "resilient randomized quantized consensus"
    - "r robustness and r s robustness of circulant graphs"
    - "distributed optimization under adversarial nodes"
    - "robustness and algebraic connectivity of random interdependent networks"

Connected through hyperedge HE-2 (induced by source paper "exact byzantine consensus on undirected graphs under local broadcast model"):
  with members:
    - "byzantine consensus under local broadcast model tight sufficient condition"
    - "exact byzantine consensus under local broadcast model"
    - "exact byzantine consensus in directed graphs"
    - "broadcast using certified propagation algorithm in presence of byzantine faults"
    - "iterative approximate byzantine consensus under a generalized fault model"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"
    - "robustness of information diffusion algorithms to locally bounded adversaries"

Connected through hyperedge HE-3 (induced by source paper "distributed learning with adversarial agents under relaxed network condition"):
  with members:
    - "defending non bayesian learning against adversarial attacks"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "distributed detection over noisy networks large deviations analysis"

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 "recent results on fault tolerant consensus in message passing networks"):
  with members:
    - "xft practical fault tolerance beyond crashes"
    - "gracefully degrading consensus and k set agreement in directed dynamic networks"
    - "approximate consensus in highly dynamic networks the role of averaging algorithms"
    - "iterative approximate consensus in the presence of byzantine link failures"
    - "asynchronous convex consensus in the presence of crash faults"
    - "reaching approximate byzantine consensus with multi hop communication"
    - "byzantine vector consensus in complete graphs"
    - "paxoslease diskless paxos for leases"

Connected through hyperedge HE-6 (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"
    - "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"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"

Connected through hyperedge HE-7 (induced by source paper "sparse spectrum sensing in infrastructure less cognitive radio networks via binary consensus algorithms"):
  with members:
    - "censoring for improved sensing performance in infrastructure less cognitive radio networks"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-8 (induced by source paper "relaxed byzantine vector consensus"):
  with members:
    - "asynchronous convex consensus in the presence of crash faults"
    - "byzantine vector consensus in complete graphs"
    - "byzantine convex consensus an optimal algorithm"

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)