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: "a polynomial time approximation algorithm for all terminal network reliability"
  Abstract: We give a fully polynomial-time randomized approximation scheme (FPRAS) for the all-terminal network reliability problem, which is to determine the probability that, in a undirected graph, assuming each edge fails independently, the remaining graph is still connected. Our main contribution is to confirm a conjecture by Gorodezky and Pak (Random Struct. Algorithms, 2014), that the expected running time of the "cluster-popping" algorithm in bi-directed graphs is bounded by a polynomial in the size of the input.

Connected through hyperedge HE-1 (induced by source paper "perfect sampling from spatial mixing"):
  with members:
    - "dynamic sampling from graphical models"
    - "on local distributed sampling and counting"
    - "approximately counting bases of bicircular matroids"
    - "improved bounds for randomly sampling colorings via linear programming"
    - "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 "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"
    - "what can be sampled locally"
    - "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-3 (induced by source paper "tight bounds for popping algorithms"):
  with members:
    - "approximately counting bases of bicircular matroids"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "uniform sampling through the lov asz local lemma"
    - "a faster approximation algorithm for the gibbs partition function"
    - "approximate counting the lovasz local lemma and inference in graphical models"
    - "a lower bound for the distributed lov asz local lemma"

Connected through hyperedge HE-4 (induced by source paper "counting hypergraph colorings in the local lemma regime"):
  with members:
    - "improved bounds for randomly sampling colorings via linear programming"
    - "uniform sampling through the lov asz local lemma"
    - "approximate counting the lovasz local lemma and inference in graphical models"
    - "commutativity in the algorithmic lovasz local lemma"
    - "an algorithmic proof of the lovasz local lemma via resampling oracles"
    - "approximation via correlation decay when strong spatial mixing fails"
    - "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)