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: "epsilon kernel coresets for stochastic points"
  Abstract: With the dramatic growth in the number of application domains that generate probabilistic, noisy and uncertain data, there has been an increasing interest in designing algorithms for geometric or combinatorial optimization problems over such data. In this paper, we initiate the study of constructing $\epsilon$-kernel coresets for uncertain points. We consider uncertainty in the existential model where each point's location is fixed but only occurs with a certain probability, and the locational model where each point has a probability distribution describing its location. An $\epsilon$-kernel coreset approximates the width of a point set in any direction. We consider approximating the expected width (an \expkernel), as well as the probability distribution on the width (an \probkernel) for any direction. We show that there exists a set of $O(1/\epsilon^{(d-1)/2})$ deterministic points which approximate the expected width under the existential and locational models, and we provide efficient algorithms for constructing such coresets. We show, however, it is not always possible to find a subset of the original uncertain points which provides such an approximation. However, if the existential probability of each point is lower bounded by a constant, an exp-kernel\ (or an fpow-kernel) is still possible. We also construct an quant-kernel coreset in linear time. Finally, combining with known techniques, we show a few applications to approximating the extent of uncertain functions, maintaining extent measures for stochastic moving points and some shape fitting problems under uncertainty.

Connected through hyperedge HE-1 (induced by source paper "computing shapley values for mean width in 3 d"):
  with members:
    - "computing shapley values in the plane"
    - "on the expected diameter width and complexity of a stochastic convex hull"
    - "batched point location in sinr diagrams via algebraic tools"
    - "convex hulls under uncertainty"

Connected through hyperedge HE-2 (induced by source paper "real time eeg classification via coresets for bci applications"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "coresets and sketches"
    - "geometric median in nearly linear time"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "randomized composable core sets for distributed submodular maximization"
    - "on the sensitivity of shape fitting problems"
    - "simple and deterministic matrix sketching"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-3 (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"
    - "convex hulls under uncertainty"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"

Connected through hyperedge HE-4 (induced by source paper "coresets and sketches"):
  with members:
    - "coresets for k means and k median clustering and their applications"
    - "optimal quantile approximation in streams"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "the optimal sample complexity of pac learning"
    - "input sparsity and hardness for robust subspace approximation"
    - "sparse approximation via generating point sets"
    - "frequent directions simple and deterministic matrix sketching"
    - "dimensionality reduction for k means clustering and low rank approximation"

Connected through hyperedge HE-5 (induced by source paper "computing the expected value and variance of geometric measures"):
  with members:
    - "convex hulls under uncertainty"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"

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)