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: "coresets for k means and k median clustering and their applications"
  Abstract: $\renewcommand{\Re}{{\rm I\!\hspace{-0.025em} R}} \newcommand{\eps}{{\varepsilon}} \newcommand{\Coreset}{{\mathcal{S}}} $ In this paper, we show the existence of small coresets for the problems of computing $k$-median and $k$-means clustering for points in low dimension. In other words, we show that given a point set $P$ in $\Re^d$, one can compute a weighted set $\Coreset \subseteq P$, of size $O(k \eps^{-d} \log{n})$, such that one can compute the $k$-median/means clustering on $\Coreset$ instead of on $P$, and get an $(1+\eps)$-approximation. #R##N#As a result, we improve the fastest known algorithms for $(1+\eps)$-approximate $k$-means and $k$-median clustering. Our algorithms have linear running time for a fixed $k$ and $\eps$. In addition, we can maintain the $(1+\eps)$-approximate $k$-median or $k$-means clustering of a stream when points are being only inserted, using polylogarithmic space and update time.

Connected through hyperedge HE-1 (induced by source paper "rk means fast clustering for relational data"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "on functional aggregate queries with additive inequalities"
    - "worst case optimal join algorithms techniques results and open problems"
    - "constraint solving via fractional edge covers"
    - "clustering high dimensional dynamic data streams"
    - "in database learning with sparse tensors"
    - "juggling functions inside a database"

Connected through hyperedge HE-2 (induced by source paper "turning big data into tiny data constant size coresets for k means pca and projective clustering"):
  with members:
    - "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"
    - "on the sensitivity of shape fitting problems"

Connected through hyperedge HE-3 (induced by source paper "coresets and sketches"):
  with members:
    - "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"
    - "epsilon kernel coresets for stochastic points"
    - "dimensionality reduction for k means clustering and low rank approximation"

Connected through hyperedge HE-4 (induced by source paper "the non uniform k center problem"):
  with members:
    - "firefighting on trees beyond integrality gaps"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "approximating k median via pseudo approximation"

Connected through hyperedge HE-5 (induced by source paper "complexity and approximation of the fuzzy k means problem"):
  with members:
    - "the hardness of approximation of euclidean k means"
    - "a theoretical and experimental comparison of the em and sem algorithm"

Connected through hyperedge HE-6 (induced by source paper "net and prune a linear time algorithm for euclidean distance problems"):
  with members:
    - "fast clustering with lower bounds no customer too far no shop too small"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-7 (induced by source paper "fault tolerant clustering revisited"):
  with members:
    - "approximating k median via pseudo approximation"

Connected through hyperedge HE-8 (induced by source paper "achieving approximate soft clustering in data streams"):
  with members:
    - "optimal time bounds for approximate clustering"
    - "learning mixtures of gaussians using the k means algorithm"

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)