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 "a topological perspective on distributed network algorithms"):
  Abstract: More than two decades ago, combinatorial topology was shown to be useful for analyzing distributed fault-tolerant algorithms in shared memory systems and in message passing systems. In this work, we show that combinatorial topology can also be useful for analyzing distributed algorithms in networks of arbitrary structure. To illustrate this, we analyze consensus, set-agreement, and approximate agreement in networks, and derive lower bounds for these problems under classical computational settings, such as the LOCAL model and dynamic networks.

Includes members:
  Member "topological characterization of consensus under general message adversaries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "k set agreement bounds in round based models through combinatorial topology")
  Member "lower bounds for maximal matchings and maximal independent sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed maximum matching verification in congest")
  Member "why extension based proofs fail":
    also connected through other hyperedges:
      (none)
  Member "tight bounds for asymptotic and approximate consensus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast multidimensional asymptotic and approximate consensus")
  Member "locally iterative distributed delta 1 coloring below szegedy vishwanathan barrier and applications to self stabilization and to restricted bandwidth models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deterministic distributed dominating set approximation in the congest model")
  Member "deterministic distributed edge coloring via hypergraph maximal matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed maximum matching verification in congest")
  Member "on the complexity of local distributed graph problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "gracefully degrading consensus and k set agreement in directed dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "consensus in rooted dynamic networks with short lived stability")

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)