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 simple d 2 sampling based ptas for k means and other clustering problems"
  Abstract: Given a set of points $P \subset \mathbb{R}^d$, the $k$-means clustering problem is to find a set of $k$ {\em centers} $C = \{c_1,...,c_k\}, c_i \in \mathbb{R}^d,$ such that the objective function $\sum_{x \in P} d(x,C)^2$, where $d(x,C)$ denotes the distance between $x$ and the closest center in $C$, is minimized. This is one of the most prominent objective functions that have been studied with respect to clustering. #R##N#$D^2$-sampling \cite{ArthurV07} is a simple non-uniform sampling technique for choosing points from a set of points. It works as follows: given a set of points $P \subseteq \mathbb{R}^d$, the first point is chosen uniformly at random from $P$. Subsequently, a point from $P$ is chosen as the next sample with probability proportional to the square of the distance of this point to the nearest previously sampled points. #R##N#$D^2$-sampling has been shown to have nice properties with respect to the $k$-means clustering problem. Arthur and Vassilvitskii \cite{ArthurV07} show that $k$ points chosen as centers from $P$ using $D^2$-sampling gives an $O(\log{k})$ approximation in expectation. Ailon et. al. \cite{AJMonteleoni09} and Aggarwal et. al. \cite{AggarwalDK09} extended results of \cite{ArthurV07} to show that $O(k)$ points chosen as centers using $D^2$-sampling give $O(1)$ approximation to the $k$-means objective function with high probability. In this paper, we further demonstrate the power of $D^2$-sampling by giving a simple randomized $(1 + \epsilon)$-approximation algorithm that uses the $D^2$-sampling in its core.

Connected through hyperedge HE-1 (induced by source paper "simple and sharp analysis of k means"):
  with members:
    - "noisy greedy and not so greedy k means"
    - "submodular optimization in the mapreduce model"
    - "a constant factor bi criteria approximation guarantee for k means"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "improved and simplified inapproximability for k means"
    - "the hardness of approximation of euclidean k means"
    - "a new framework for distributed submodular maximization"
    - "a comparative study of efficient initialization methods for the k means clustering algorithm"

Connected through hyperedge HE-2 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "verdictdb universalizing approximate query processing"
    - "measuring the effects of data parallelism on neural network training"
    - "hemingway modeling distributed optimization algorithms"
    - "unbiased estimates for linear regression via volume sampling"
    - "database learning toward a database that becomes smarter every time"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-3 (induced by source paper "streaming ptas for binary ell_0 low rank approximation"):
  with members:
    - "streaming ptas for constrained k means"
    - "approximation schemes for low rank binary matrix approximation problems"
    - "a unified framework for clustering constrained data without locality property"
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "generalized independent component analysis over finite alphabets"
    - "faster algorithms for the constrained k means problem"
    - "low rank approximation of binary matrices column subset selection and generalizations"

Connected through hyperedge HE-4 (induced by source paper "metric 1 median selection query complexity vs approximation ratio"):
  with members:
    - "metric 1 median selection with fewer queries"
    - "a deterministic sublinear time nonadaptive algorithm for metric 1 median selection"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "a lower bound for metric 1 median selection"
    - "optimal time bounds for approximate clustering"
    - "fast approximation of centrality"

Connected through hyperedge HE-5 (induced by source paper "a lower bound for metric 1 median selection"):
  with members:
    - "metric 1 median selection query complexity vs approximation ratio"
    - "a deterministic sublinear time nonadaptive algorithm for metric 1 median selection"
    - "approximating k median via pseudo approximation"
    - "optimal time bounds for approximate clustering"
    - "fast approximation of centrality"

Connected through hyperedge HE-6 (induced by source paper "a tight lower bound instance for k means in constant dimension"):
  with members:
    - "scalable k means"

Connected through hyperedge HE-7 (induced by source paper "deterministic feature selection for k means clustering"):
  with members:
    - "near optimal column based matrix reconstruction"
    - "random projections for k means clustering"
    - "row sampling for matrix algorithms via a non commutative bernstein bound"
    - "twice ramanujan sparsifiers"
    - "relative error cur matrix decompositions"
    - "a direct formulation for sparse pca using semidefinite programming"

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)