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 "an exponential separation between randomized and deterministic complexity in the local model"):
  Abstract: Over the past 30 years numerous algorithms have been designed for symmetry breaking problems in the LOCAL model, such as maximal matching, MIS, vertex coloring, and edge-coloring. For most problems the best randomized algorithm is at least exponentially faster than the best deterministic algorithm. In this paper we prove that these exponential gaps are necessary and establish connections between the deterministic and randomized complexities in the LOCAL model. Each result has a very compelling take-away message: #R##N#1. Fast $\Delta$-coloring of trees requires random bits: Building on the recent lower bounds of Brandt et al., we prove that the randomized complexity of $\Delta$-coloring a tree with maximum degree $\Delta\ge 55$ is $\Theta(\log_\Delta\log n)$, whereas its deterministic complexity is $\Theta(\log_\Delta n)$ for any $\Delta\ge 3$. This also establishes a large separation between the deterministic complexity of $\Delta$-coloring and $(\Delta+1)$-coloring trees. #R##N#2. Randomized lower bounds imply deterministic lower bounds: We prove that any deterministic algorithm for a natural class of problems that runs in $O(1)+o(\log_\Delta n)$ rounds can be transformed to run in $O(\log^*n-\log^*\Delta+1)$ rounds. If the transformed algorithm violates a lower bound (even allowing randomization), then one can conclude that the problem requires $\Omega(\log_\Delta n)$ time deterministically. #R##N#3. Deterministic lower bounds imply randomized lower bounds: We prove that the randomized complexity of any natural problem on instances of size $n$ is at least its deterministic complexity on instances of size $\sqrt{\log n}$. This shows that a deterministic $\Omega(\log_\Delta n)$ lower bound for any problem implies a randomized $\Omega(\log_\Delta\log n)$ lower bound. It also illustrates that the graph shattering technique is absolutely essential to the LOCAL model.

Includes members:
  Member "distributed degree splitting edge coloring and orientations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "local conflict coloring":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "an improved distributed algorithm for maximal independent set":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")
  Member "a lower bound for the distributed lov asz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "toward more localized local algorithms removing assumptions concerning global knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "faster deterministic distributed coloring through recursive list coloring")
  Member "the locality of distributed symmetry breaking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed maximum matching verification in congest")
  Member "super fast 3 ruling sets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed computation in node capacitated networks")
  Member "local computation lower and upper bounds":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed maximum matching verification in congest")

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.CC(Computational Complexity)