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 "effects of topology knowledge and relay depth on asynchronous consensus"):
  Abstract: Consider a point-to-point message-passing network. We are interested in the asynchronous crash-tolerant consensus problem in incomplete networks. We study the feasibility and efficiency of approximate consensus under different restrictions on topology knowledge and the relay depth, i.e., the maximum number of hops any message can be relayed. These two constraints are common in large-scale networks, and are used to avoid memory overload and network congestion respectively. Specifically, for different values of integers k, k , we consider that each node knows all its neighbors of at most k-hop distance (k-hop topology knowledge), and the relay depth is k . We consider both directed and undirected graphs. More concretely, we answer the following main question in asynchronous systems: #R##N#What is a tight condition on the underlying communication graphs for achieving approximate consensus if each node has only a k-hop topology knowledge and relay depth k? #R##N#To prove that the necessary conditions presented in the paper are also sufficient, we have developed algorithms that achieve consensus in graphs satisfying those conditions: #R##N#-The first class of algorithms requires k-hop topology knowledge and relay depth k. Unlike prior algorithms, these algorithms do not flood the network, and each node does not need the full topology knowledge. We show how the convergence time and the message complexity of those algorithms is affected by k, providing the respective upper bounds. #R##N#-The second set of algorithms requires only one-hop neighborhood knowledge, i.e., immediate incoming and outgoing neighbors, but needs to flood the network (i.e., relay depth is n, where n is the number of nodes). One result that may be of independent interest is a topology discovery mechanism to learn and "estimate" the topology in asynchronous directed networks with crash faults.

Includes members:
  Member "gracefully degrading consensus and k set agreement in directed dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a topological perspective on distributed network algorithms")
  Member "reliable communication in a dynamic network in the presence of byzantine faults":
    also connected through other hyperedges:
      (none)
  Member "approximate consensus in highly dynamic networks the role of averaging algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "k set agreement bounds in round based models through combinatorial topology")
  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 "consensus of multi agent networks in the presence of adversaries using only local information":
    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 approximate byzantine consensus under a generalized fault model":
    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 in arbitrary directed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "grand challenges of resilience autonomous system resilience through design and runtime measures")
  Member "robustness of complex networks with implications for consensus and contagion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the strength of connectivity of random graphs induced by pairwise key predistribution schemes implications on security and reliability of heterogeneous sensor networks")

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)