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: "byzantine vector consensus in complete graphs"
  Abstract: Consider a network of n processes each of which has a d-dimensional vector of reals as its input. Each process can communicate directly with all the processes in the system; thus the communication network is a complete graph. All the communication channels are reliable and FIFO (first-in-first-out). The problem of Byzantine vector consensus (BVC) requires agreement on a d-dimensional vector that is in the convex hull of the d-dimensional input vectors at the non-faulty processes. We obtain the following results for Byzantine vector consensus in complete graphs while tolerating up to f Byzantine failures: #R##N#* We prove that in a synchronous system, n >= max(3f+1, (d+1)f+1) is necessary and sufficient for achieving Byzantine vector consensus. #R##N#* In an asynchronous system, it is known that exact consensus is impossible in presence of faulty processes. For an asynchronous system, we prove that n >= (d+2)f+1 is necessary and sufficient to achieve approximate Byzantine vector consensus. #R##N#Our sufficiency proofs are constructive. We show sufficiency by providing explicit algorithms that solve exact BVC in synchronous systems, and approximate BVC in asynchronous systems. #R##N#We also obtain tight bounds on the number of processes for achieving BVC using algorithms that are restricted to a simpler communication pattern.

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 "byzantine approximate agreement on graphs"):
  with members:
    - "lattice agreement in message passing systems"
    - "self stabilising byzantine clock synchronisation is almost as easy as consensus"
    - "near optimal self stabilising counting and firing squads"
    - "easy impossibility proofs for k set agreement in message passing systems"

Connected through hyperedge HE-3 (induced by source paper "determining r robustness of arbitrary digraphs using zero one linear integer programming"):
  with members:
    - "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"
    - "distributed optimization under adversarial nodes"
    - "robustness and algebraic connectivity of random interdependent networks"
    - "reaching approximate byzantine consensus with multi hop communication"
    - "resilient continuous time consensus in fractional robust networks"
    - "iterative approximate byzantine consensus under a generalized fault model"

Connected through hyperedge HE-4 (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"
    - "matrix representation of iterative approximate byzantine consensus in directed 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-5 (induced by source paper "byzantine preferential voting"):
  with members:
    - "complexity of and algorithms for borda manipulation"

Connected through hyperedge HE-6 (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-7 (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"
    - "iterative byzantine vector consensus in incomplete graphs"
    - "paxoslease diskless paxos for leases"

Connected through hyperedge HE-8 (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"
    - "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"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"

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)