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 local distributed sampling and counting"
  Abstract: In classic distributed graph problems, each instance on a graph specifies a space of feasible solutions (e.g. all proper ($\Delta+1$)-list-colorings of the graph), and the task of distributed algorithm is to construct a feasible solution using local information. #R##N#We study distributed sampling and counting problems, in which each instance specifies a joint distribution of feasible solutions. The task of distributed algorithm is to sample from this joint distribution, or to locally measure the volume of the probability space via the marginal probabilities. The latter task is also known as inference, which is a local counterpart of counting. #R##N#For self-reducible classes of instances, the following equivalences are established in the LOCAL model up to polylogarithmic factors: #R##N#$\bullet$ For all joint distributions, approximate inference and approximate sampling are computationally equivalent. #R##N#$\bullet$ For all joint distributions defined by local constraints, exact sampling is reducible to either one of the above tasks. #R##N#$\bullet$ If further, sequentially constructing a feasible solution is trivial locally, then all above tasks are easy if and only if the joint distribution exhibits strong spatial mixing. #R##N#Combining with the state of the arts of strong spatial mixing, we obtain efficient sampling algorithms in the LOCAL model for various important sampling problems, including: an $O(\sqrt{\Delta}\log^3n)$-round algorithm for exact sampling matchings in graphs with maximum degree $\Delta$, and an $O(\log^3n)$-round algorithm for sampling according to the hardcore model (weighted independent sets) in the uniqueness regime, which along with the $\Omega(\mathrm{diam})$ lower bound in arXiv:1702.00142 for sampling according to the hardcore model in the non-uniqueness regime, gives the first computational phase transition for distributed sampling.

Connected through hyperedge HE-1 (induced by source paper "perfect sampling from spatial mixing"):
  with members:
    - "dynamic sampling from graphical models"
    - "approximately counting bases of bicircular matroids"
    - "improved bounds for randomly sampling colorings via linear programming"
    - "a polynomial time approximation algorithm for all terminal network reliability"
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"
    - "approximate counting via correlation decay on planar graphs"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"
    - "correlation decay up to uniqueness in spin systems"

Connected through hyperedge HE-2 (induced by source paper "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"):
  with members:
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "what can be sampled locally"
    - "sampling random colorings of sparse random graphs"
    - "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 "what can be sampled locally"):
  with members:
    - "distributed degree splitting edge coloring and orientations"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "bis hardness for 2 spin systems on bipartite bounded degree graphs in the tree non uniqueness region"
    - "uniform sampling through the lov asz local lemma"
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"
    - "an exponential separation between randomized and deterministic complexity in the local model"
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"
    - "on the complexity of local distributed graph problems"

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.DS(Data Structures and Algorithms)