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: "what can be sampled locally"
  Abstract: The local computation of Linial [FOCS'87] and Naor and Stockmeyer [STOC'93] concerns with the question of whether a locally definable distributed computing problem can be solved locally: for a given local CSP whether a CSP solution can be constructed by a distributed algorithm using local information. In this paper, we consider the problem of sampling a uniform CSP solution by distributed algorithms, and ask whether a locally definable joint distribution can be sampled from locally. More broadly, we consider sampling from Gibbs distributions induced by weighted local CSPs in the LOCAL model. #R##N#We give two Markov chain based distributed algorithms which we believe to represent two fundamental approaches for sampling from Gibbs distributions via distributed algorithms. The first algorithm generically parallelizes the single-site sequential Markov chain by iteratively updating a random independent set of variables in parallel, and achieves an $O(\Delta\log n)$ time upper bound in the LOCAL model, where $\Delta$ is the maximum degree, when the Dobrushin's condition for the Gibbs distribution is satisfied. The second algorithm is a novel parallel Markov chain which proposes to update all variables simultaneously yet still guarantees to converge correctly with no bias. It surprisingly parallelizes an intrinsically sequential process: stabilizing to a joint distribution with massive local dependencies, and may achieve an optimal $O(\log n)$ time upper bound independent of the maximum degree $\Delta$ under a stronger mixing condition. #R##N#We also show a strong $\Omega(diam)$ lower bound for sampling independent set in graphs with maximum degree $\Delta\ge 6$. This lower bound holds even when every node is aware of the graph. This gives a strong separation between sampling and constructing locally checkable labelings.

Connected through hyperedge HE-1 (induced by source paper "distributed metropolis sampler with optimal parallelism"):
  with members:
    - "dynamic sampling from graphical models"
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "hogwild gibbs can be panaccurate"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-2 (induced by source paper "fully asynchronous distributed metropolis sampler with optimal speedup"):
  with members:
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-3 (induced by source paper "dynamic sampling from graphical models"):
  with members:
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "tight bounds for popping algorithms"
    - "sublogarithmic distributed algorithms for lov asz local lemma and the complexity hierarchy"
    - "a polynomial time approximation algorithm for all terminal network reliability"
    - "uniform sampling through the lov asz local lemma"
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"

Connected through hyperedge HE-4 (induced by source paper "on local distributed sampling and counting"):
  with members:
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
    - "on derandomizing local distributed algorithms"
    - "uniform sampling through the lov asz local lemma"
    - "on the complexity of local distributed graph problems"
    - "counting hypergraph matchings up to uniqueness threshold"
    - "robust probabilistic inference in distributed systems"
    - "correlation decay up to uniqueness in spin systems"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "a simple parallel and distributed sampling technique local glauber dynamics"):
  with members:
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
    - "rapid mixing of glauber dynamics for colorings below vigoda s 11 6 threshold"
    - "linear programming bounds for randomly sampling colorings"
    - "uniform sampling through the lov asz local lemma"

Connected through hyperedge HE-7 (induced by source paper "uniform sampling through the lov asz local lemma"):
  with members:
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"
    - "approximate counting the lovasz local lemma and inference in graphical models"
    - "an algorithmic proof of the lovasz local lemma via resampling oracles"
    - "approximation via correlation decay when strong spatial mixing fails"
    - "fptas for counting monotone cnf"
    - "new constructive aspects of the lovasz local lemma"
    - "a constructive proof of the general 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.DS(Data Structures and Algorithms)