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: "sampling random colorings of sparse random graphs"
  Abstract: We study the mixing properties of the single-site Markov chain known as the Glauber dynamics for sampling $k$-colorings of a sparse random graph $G(n,d/n)$ for constant $d$. The best known rapid mixing results for general graphs are in terms of the maximum degree $\Delta$ of the input graph $G$ and hold when $k>11\Delta/6$ for all $G$. Improved results hold when $k>\alpha\Delta$ for graphs with girth $\geq 5$ and $\Delta$ sufficiently large where $\alpha\approx 1.7632\ldots$ is the root of $\alpha=\exp(1/\alpha)$; further improvements on the constant $\alpha$ hold with stronger girth and maximum degree assumptions. For sparse random graphs the maximum degree is a function of $n$ and the goal is to obtain results in terms of the expected degree $d$. The following rapid mixing results for $G(n,d/n)$ hold with high probability over the choice of the random graph for sufficiently large constant~$d$. Mossel and Sly (2009) proved rapid mixing for constant $k$, and Efthymiou (2014) improved this to $k$ linear in~$d$. The condition was improved to $k>3d$ by Yin and Zhang (2016) using non-MCMC methods. Here we prove rapid mixing when $k>\alpha d$ where $\alpha\approx 1.7632\ldots$ is the same constant as above. Moreover we obtain $O(n^{3})$ mixing time of the Glauber dynamics, while in previous rapid mixing results the exponent was an increasing function in $d$. As in previous results for random graphs our proof analyzes an appropriately defined block dynamics to "hide" high-degree vertices. One new aspect in our improved approach is utilizing so-called local uniformity properties for the analysis of block dynamics. To analyze the "burn-in" phase we prove a concentration inequality for the number of disagreements propagating in large blocks.

Connected through hyperedge HE-1 (induced by source paper "counting solutions to random cnf formulas"):
  with members:
    - "counting independent sets and colorings on random regular bipartite graphs"
    - "counting hypergraph colorings in the local lemma regime"
    - "approximate counting the lovasz local lemma and inference in graphical models"
    - "approximation via correlation decay when strong spatial mixing fails"
    - "on the concentration of the number of solutions of random satisfiability formulas"
    - "new constructive aspects of the lovasz local lemma"
    - "deterministic algorithms for the lovasz local lemma"
    - "a constructive proof of the general lovasz local lemma"

Connected through hyperedge HE-2 (induced by source paper "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"):
  with members:
    - "on local distributed sampling and counting"
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "what can be sampled locally"
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"
    - "on the complexity of local distributed graph problems"
    - "the locality of distributed symmetry breaking"
    - "deterministic distributed vertex coloring in polylogarithmic time"

Connected through hyperedge HE-3 (induced by source paper "sampling in uniqueness from the potts and random cluster models on random regular graphs"):
  with members:
    - "algorithms for bis hard problems on expander graphs"
    - "algorithmic pirogov sinai theory"
    - "spatial mixing and the connective constant optimal bounds"
    - "mixing of the glauber dynamics for the ferromagnetic potts model"
    - "spatial mixing and approximation algorithms for graphs with bounded connective constant"
    - "ferromagnetic potts model refined bis hardness and related results"
    - "approximating partition functions of two state spin systems"

Connected through hyperedge HE-4 (induced by source paper "uniqueness for the 3 state antiferromagnetic potts model on the tree"):
  with members:
    - "sampling in uniqueness from the potts and random cluster models on random regular graphs"
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"
    - "an fptas for counting proper four colorings on cubic graphs"
    - "spatial mixing and the connective constant optimal bounds"
    - "correlation decay up to uniqueness in spin systems"

Connected through hyperedge HE-5 (induced by source paper "rapid mixing of glauber dynamics for colorings below vigoda s 11 6 threshold"):
  with members:
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"

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.DM(Discrete Mathematics)