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: "on the complexity of distributed splitting problems"
  Abstract: One of the fundamental open problems in the area of distributed graph algorithms is whether randomization is needed for efficient symmetry breaking. While there are poly log n-time randomized algorithms for all the classic symmetry breaking problems, for many of them, the best deterministic algorithms are almost exponentially slower. The following basic local splitting problem, which is known as weak splitting, takes a central role in this context: Each node of a graph G=(V,E) has to be colored red or blue such that each node of sufficiently large degree has at least one neighbor of each color. Ghaffari, Kuhn, and Maus [STOC '17] showed that this seemingly simple problem is complete w.r.t. the above fundamental open question in the following sense: If there is an efficient poly log n-time determinstic distributed algorithm for weak splitting, then there is such an algorithm for all locally checkable graph problems for which an efficient randomized algorithm exists. We investigate the distributed complexity of weak splitting and some closely related problems and we in particular obtain the following results:   We obtain efficient algorithms for special cases of weak splitting in nearly regular graphs. We show that if δ=O(log n) and Δ are the minimum and maximum degrees of G, weak splitting can be solved deterministically in time O #916;(√ over δ • poly(log n)). Further, if δ = O(log log n) and Δ ≤ 2e δ, the time complexity is O(Δ over δ⋅poly(log log n)).   We prove that the following two related problems are also complete in the same sense: (I) Color the nodes of a graph with C ≤ poly log n colors such that each node with a sufficiently large polylogarithmic degree has at least 2 log n different colors among its neighbors, and (II) Color the nodes with a large constant number of colors so that for each node of a sufficiently large at least logarithmic degree d(v), the number of neighbors of each color is at most (1-ed(v) for some constant e > 0.

Connected through hyperedge HE-1 (induced by source paper "a sharp threshold phenomenon for the distributed complexity of the lovasz local lemma"):
  with members:
    - "polylogarithmic time deterministic network decomposition and distributed derandomization"
    - "distributed approximation algorithms for maximum matching in graphs and hypergraphs"
    - "improved distributed degree splitting and edge coloring"
    - "a time hierarchy theorem for the local model"
    - "on derandomizing local distributed algorithms"
    - "distributed degree splitting edge coloring and orientations"
    - "an exponential separation between randomized and deterministic complexity in the local model"
    - "on the complexity of local distributed graph problems"

Connected through hyperedge HE-2 (induced by source paper "deterministic coloring algorithms in the local model"):
  with members:
    - "on the use of randomness in local distributed graph algorithms"
    - "on derandomizing local distributed algorithms"
    - "on the complexity of local distributed graph problems"
    - "deterministic algorithms for the lovasz local lemma"

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.DC(Distributed, Parallel, and Cluster Computing)