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: "approximating the expected values for combinatorial optimization problems over stochastic points"
  Abstract: We consider the stochastic geometry model where the location of each node is a random point in a given metric space, or the existence of each node is uncertain. We study the problems of computing the expected lengths of several combinatorial or geometric optimization problems over stochastic points, including closest pair, minimum spanning tree, $k$-clustering, minimum perfect matching, and minimum cycle cover. We also consider the problem of estimating the probability that the length of closest pair, or the diameter, is at most, or at least, a given threshold. Most of the above problems are known to be $\sharpP$-hard. We obtain FPRAS (Fully Polynomial Randomized Approximation Scheme) for most of them in both the existential and locational uncertainty models. Our result for stochastic minimum spanning trees in the locational uncertain model improves upon the previously known constant factor approximation algorithm. Our results for other problems are the first known to the best of our knowledge.

Connected through hyperedge HE-1 (induced by source paper "on the expected diameter width and complexity of a stochastic convex hull"):
  with members:
    - "stochastic closest pair problem and most likely nearest neighbor search in tree spaces"
    - "colored stochastic dominance problems"
    - "nearest neighbor searching under uncertainty ii"
    - "on the separability of stochastic geometric objects with applications"
    - "epsilon kernel coresets for stochastic points"
    - "convex hulls under uncertainty"

Connected through hyperedge HE-2 (induced by source paper "stochastic k center and j flat center problems"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "nearest neighbor searching under uncertainty ii"
    - "range queries on uncertain data"
    - "convex hulls under uncertainty"
    - "range counting coresets for uncertain data"
    - "on the sensitivity of shape fitting problems"
    - "a unified framework for approximating and clustering data"
    - "minimum enclosing polytope in high dimensions"

Connected through hyperedge HE-3 (induced by source paper "stochastic closest pair problem and most likely nearest neighbor search in tree spaces"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "on the separability of stochastic geometric objects with applications"
    - "convex hulls under uncertainty"
    - "the geometric stability of voronoi diagrams with respect to small changes of the sites"

Connected through hyperedge HE-4 (induced by source paper "approximating the distribution of the median and other robust estimators on uncertain data"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "convex hulls under uncertainty"
    - "range counting coresets for uncertain data"

Connected through hyperedge HE-5 (induced by source paper "computing the expected value and variance of geometric measures"):
  with members:
    - "epsilon kernel coresets for stochastic points"
    - "convex hulls under uncertainty"

Connected through hyperedge HE-6 (induced by source paper "epsilon kernel coresets for stochastic points"):
  with members:
    - "range queries on uncertain data"
    - "convex hulls under uncertainty"
    - "range counting coresets for uncertain data"
    - "flow computations on imprecise terrains"
    - "a unified framework for approximating and clustering data"
    - "on the expected complexity of random convex hulls"

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)