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 "classification of distributed binary labeling problems"):
  Abstract: We present a complete classification of the deterministic distributed time complexity for a family of graph problems: binary labeling problems in trees. These are locally checkable problems that can be encoded with an alphabet of size two in the edge labeling formalism. Examples of binary labeling problems include sinkless orientation, sinkless and sourceless orientation, 2-vertex coloring, perfect matching, and the task of coloring edges of red and blue such that all nodes are incident to at least one red and at least one blue edge. More generally, we can encode e.g. any cardinality constraints on indegrees and outdegrees. #R##N#We study the deterministic time complexity of solving a given binary labeling problem in trees, in the usual LOCAL model of distributed computing. We show that the complexity of any such problem is in one of the following classes: $O(1)$, $\Theta(\log n)$, $\Theta(n)$, or unsolvable. In particular, a problem that can be represented in the binary labeling formalism cannot have time complexity $\Theta(\log^* n)$, and hence we know that e.g. any encoding of maximal matchings has to use at least three labels (which is tight). #R##N#Furthermore, given the description of any binary labeling problem, we can easily determine in which of the four classes it is and what is an asymptotically optimal algorithm for solving it. Hence the distributed time complexity of binary labeling problems is decidable, not only in principle, but also in practice: there is a simple and efficient algorithm that takes the description of a binary labeling problem and outputs its distributed time complexity.

Includes members:
  Member "seeing far vs seeing wide volume complexity of local graph problems":
    also connected through other hyperedges:
      (none)
  Member "an automatic speedup theorem for distributed problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "truly tight in delta bounds for bipartite maximal matching and variants")
  Member "polylogarithmic time deterministic network decomposition and distributed derandomization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "massively parallel algorithms for distance approximation and spanners")
  Member "lower bounds for maximal matchings and maximal independent sets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed maximum matching verification in congest")
  Member "how much does randomness help with locally checkable problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed graph problems through an automata theoretic lens")
  Member "almost global problems in the local model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed graph problems through an automata theoretic lens")
  Member "the distributed complexity of locally checkable problems on paths is decidable":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed graph problems through an automata theoretic lens")
  Member "hardness of minimal symmetry breaking in distributed computing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "truly tight in delta bounds for bipartite maximal matching and variants")

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)