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 time hierarchy theorem for the local model"):
  Abstract: The celebrated Time Hierarchy Theorem for Turing machines states, informally, that more problems can be solved given more time. The extent to which a time hierarchy-type theorem holds in the distributed LOCAL model has been open for many years. It is consistent with previous results that all natural problems in the LOCAL model can be classified according to a small constant number of complexities, such as $O(1),O(\log^* n), O(\log n), 2^{O(\sqrt{\log n})}$, etc. #R##N#In this paper we establish the first time hierarchy theorem for the LOCAL model and prove that several gaps exist in the LOCAL time hierarchy. #R##N#1. We define an infinite set of simple coloring problems called Hierarchical $2\frac{1}{2}$-Coloring}. A correctly colored graph can be confirmed by simply checking the neighborhood of each vertex, so this problem fits into the class of locally checkable labeling (LCL) problems. However, the complexity of the $k$-level Hierarchical $2\frac{1}{2}$-Coloring problem is $\Theta(n^{1/k})$, for $k\in\mathbb{Z}^+$. The upper and lower bounds hold for both general graphs and trees, and for both randomized and deterministic algorithms. #R##N#2. Consider any LCL problem on bounded degree trees. We prove an automatic-speedup theorem that states that any randomized $n^{o(1)}$-time algorithm solving the LCL can be transformed into a deterministic $O(\log n)$-time algorithm. Together with a previous result, this establishes that on trees, there are no natural deterministic complexities in the ranges $\omega(\log^* n)$---$o(\log n)$ or $\omega(\log n)$---$n^{o(1)}$. #R##N#3. We expose a gap in the randomized time hierarchy on general graphs. Any randomized algorithm that solves an LCL problem in sublogarithmic time can be sped up to run in $O(T_{LLL})$ time, which is the complexity of the distributed Lovasz local lemma problem, currently known to be $\Omega(\log\log n)$ and $O(\log n)$.

Includes members:
  Member "the complexity of distributed edge coloring with small palettes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "lcl problems on grids":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an automatic speedup theorem for distributed problems")
  Member "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deterministic algorithms for the lovasz local lemma simpler more general and more parallel")
  Member "polynomial lower bound for distributed graph coloring in a weak local model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "faster deterministic distributed coloring through recursive list coloring")
  Member "distributed degree splitting edge coloring and orientations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "survey of distributed decision":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what graph neural networks cannot learn depth vs width")
  Member "an exponential separation between randomized and deterministic complexity in the local model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "truly tight in delta bounds for bipartite maximal matching and variants")
  Member "on the complexity of local distributed graph problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")

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)