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 the sensitivity of shape fitting problems"
  Abstract: In this article, we study shape fitting problems, $\epsilon$-coresets, and total sensitivity. We focus on the $(j,k)$-projective clustering problems, including $k$-median/$k$-means, $k$-line clustering, $j$-subspace approximation, and the integer $(j,k)$-projective clustering problem. We derive upper bounds of total sensitivities for these problems, and obtain $\epsilon$-coresets using these upper bounds. Using a dimension-reduction type argument, we are able to greatly simplify earlier results on total sensitivity for the $k$-median/$k$-means clustering problems, and obtain positively-weighted $\epsilon$-coresets for several variants of the $(j,k)$-projective clustering problem. We also extend an earlier result on $\epsilon$-coresets for the integer $(j,k)$-projective clustering problem in fixed dimension to the case of high dimension.

Connected through hyperedge HE-1 (induced by source paper "coresets for clustering in graphs of bounded treewidth"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "coresets for ordered weighted clustering"
    - "k means clustering of lines for big data"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "coresets and sketches"

Connected through hyperedge HE-2 (induced by source paper "strong coresets for subspace approximation and k median in nearly linear time"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "new frameworks for offline and streaming coreset constructions"
    - "input sparsity and hardness for robust subspace approximation"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"

Connected through hyperedge HE-3 (induced by source paper "sketched mindist"):
  with members:
    - "scalable spatial scan statistics for trajectories"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "simple distances for trajectories via landmarks"
    - "relative error rkhs embeddings for gaussian kernels"
    - "new frameworks for offline and streaming coreset constructions"
    - "online row sampling"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-4 (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"
    - "epsilon kernel coresets for stochastic points"
    - "simple and deterministic matrix sketching"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-5 (induced by source paper "tight sensitivity bounds for smaller coresets"):
  with members:
    - "fast and accurate least mean squares solvers"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "new frameworks for offline and streaming coreset constructions"
    - "k means for streaming and distributed big sparse data"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "provable approximations for constrained ell_p regression"):
  with members:
    - "minimizing sum of non convex but piecewise log lipschitz functions using coresets"
    - "l1 norm error function robustness and outlier regularization"
    - "new frameworks for offline and streaming coreset constructions"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-7 (induced by source paper "turning big data into tiny data constant size coresets for k means pca and projective clustering"):
  with members:
    - "coresets for k means and k median clustering and their applications"
    - "clustering high dimensional dynamic data streams"
    - "new frameworks for offline and streaming coreset constructions"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal approximate matrix product in terms of stable rank"
    - "dimensionality reduction for k means clustering and low rank approximation"

Connected through hyperedge HE-8 (induced by source paper "strong coresets for k median and subspace approximation goodbye dimension"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "new frameworks for offline and streaming coreset constructions"
    - "input sparsity and hardness for robust subspace approximation"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "randomized dimensionality reduction for k means clustering"

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.CG(Computational Geometry)