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 "exact byzantine consensus on undirected graphs under local broadcast model"):
  Abstract: This paper considers the Byzantine consensus problem for nodes with binary inputs. The nodes are interconnected by a network represented as an undirected graph, and the system is assumed to be synchronous. Under the classical point-to-point communication model, it is well-known [7] that the following two conditions are both necessary and sufficient to achieve Byzantine consensus among $n$ nodes in the presence of up to $f$ Byzantine faulty nodes: $n \ge 3f+1$ and vertex connectivity at least $2f+1$. In the classical point-to-point communication model, it is possible for a faulty node to equivocate, i.e., transmit conflicting information to different neighbors. Such equivocation is possible because messages sent by a node to one of its neighbors are not overheard by other neighbors. #R##N#This paper considers the local broadcast model. In contrast to the point-to-point communication model, in the local broadcast model, messages sent by a node are received identically by all of its neighbors. Thus, under the local broadcast model, attempts by a node to send conflicting information can be detected by its neighbors. Under this model, we show that the following two conditions are both necessary and sufficient for Byzantine consensus: vertex connectivity at least $\lfloor 3f/2 \rfloor + 1$ and minimum node degree at least $2f$. Observe that the local broadcast model results in a lower requirement for connectivity and the number of nodes $n$, as compared to the point-to-point communication model. #R##N#We extend the above results to a hybrid model that allows some of the Byzantine faulty nodes to equivocate. The hybrid model bridges the gap between the point-to-point and local broadcast models, and helps to precisely characterize the trade-off between equivocation and network requirements.

Includes members:
  Member "byzantine consensus under local broadcast model tight sufficient condition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asynchronous byzantine consensus on undirected graphs under local broadcast model")
  Member "exact byzantine consensus under local broadcast model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "byzantine consensus under local broadcast model tight sufficient condition")
  Member "iterative byzantine vector consensus in incomplete graphs":
    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 "exact byzantine consensus in directed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "byzantine consensus under local broadcast model tight sufficient condition")
  Member "broadcast using certified propagation algorithm in presence of byzantine faults":
    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 "iterative approximate byzantine consensus under a generalized fault model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "determining r and r s robustness of digraphs using mixed integer linear programming")
  Member "iterative approximate byzantine consensus in arbitrary directed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "grand challenges of resilience autonomous system resilience through design and runtime measures")
  Member "robustness of information diffusion algorithms to locally bounded adversaries":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "inner attention supported adaptive cooperation for heterogeneous multi robots teaming based on multi agent reinforcement learning")

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)