human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "truly tight in delta bounds for bipartite maximal matching and variants"):
  with members:
    - "an automatic speedup theorem for distributed problems"
    - "classification of distributed binary labeling problems"
    - "lower bounds for maximal matchings and maximal independent sets"
    - "locality of not so weak coloring"
    - "hardness of minimal symmetry breaking in distributed computing"
    - "the complexity of distributed edge coloring with small palettes"
    - "improved deterministic distributed matching via rounding"
    - "distributed maximal matching greedy is optimal"

Connected through hyperedge HE-2 (induced by source paper "distributed graph problems through an automata theoretic lens"):
  with members:
    - "classification of distributed binary labeling problems"
    - "lower bounds for maximal matchings and maximal independent sets"
    - "how much does randomness help with locally checkable problems"
    - "almost global problems in the local model"
    - "the distributed complexity of locally checkable problems on paths is decidable"
    - "new classes of distributed time complexity"
    - "a time hierarchy theorem for the local model"

Connected through hyperedge HE-3 (induced by source paper "smaller cuts higher lower bounds"):
  with members:
    - "a faster deterministic distributed algorithm for weighted apsp through pipelining"
    - "a deterministic distributed algorithm for exact weighted all pairs shortest paths in tilde o n 3 2 rounds"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "optimal distributed coloring algorithms for planar graphs in the local model"
    - "distributed exact shortest paths in sublinear time"
    - "a time hierarchy theorem for the local model"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "quadratic and near quadratic lower bounds for the congest model"

Connected through hyperedge HE-4 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems"):
  with members:
    - "an automatic speedup theorem for distributed problems"
    - "polylogarithmic time deterministic network decomposition and distributed derandomization"
    - "how much does randomness help with locally checkable problems"
    - "locality of not so weak coloring"
    - "almost global problems in the local model"
    - "the distributed complexity of locally checkable problems on paths is decidable"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "hardness of minimal symmetry breaking in distributed computing"

Connected through hyperedge HE-5 (induced by source paper "fast and simple deterministic algorithms for highly dynamic networks"):
  with members:
    - "lower bounds for maximal matchings and maximal independent sets"
    - "how much does randomness help with locally checkable problems"
    - "simple dynamic algorithms for maximal independent set and other problems"
    - "fully dynamic maximal independent set with sublinear update time"
    - "local distributed algorithms in highly dynamic networks"
    - "improved algorithms for fully dynamic maximal independent set"
    - "improved deterministic distributed matching via rounding"
    - "new classes of distributed time complexity"

Connected through hyperedge HE-6 (induced by source paper "classification of distributed binary labeling problems"):
  with members:
    - "seeing far vs seeing wide volume complexity of local graph problems"
    - "an automatic speedup theorem for distributed problems"
    - "polylogarithmic time deterministic network decomposition and distributed derandomization"
    - "lower bounds for maximal matchings and maximal independent sets"
    - "how much does randomness help with locally checkable problems"
    - "almost global problems in the local model"
    - "the distributed complexity of locally checkable problems on paths is decidable"
    - "hardness of minimal symmetry breaking in distributed computing"

Connected through hyperedge HE-7 (induced by source paper "faster deterministic distributed coloring through recursive list coloring"):
  with members:
    - "towards the locality of vizing s theorem"
    - "distributed approximation algorithms for maximum matching in graphs and hypergraphs"
    - "improved distributed delta coloring"
    - "the complexity of distributed edge coloring with small palettes"
    - "deterministic distributed edge coloring with fewer colors"
    - "deterministic distributed edge coloring via hypergraph maximal matching"
    - "adapting sequential algorithms to the distributed setting"
    - "on derandomizing local distributed algorithms"

Connected through hyperedge HE-8 (induced by source paper "an automatic speedup theorem for distributed problems"):
  with members:
    - "lower bounds for maximal matchings and maximal independent sets"
    - "almost global problems in the local model"
    - "hardness of minimal symmetry breaking in distributed computing"
    - "the complexity of distributed edge coloring with small palettes"
    - "sublogarithmic distributed algorithms for lov asz local lemma and the complexity hierarchy"
    - "new classes of distributed time complexity"
    - "a time hierarchy theorem for the local model"
    - "lcl problems on grids"

Question: Please tell me which class the center node 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)