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 variants of k means clustering"
  Abstract: \textit{Clustering problems} often arise in the fields like data mining, machine learning etc. to group a collection of objects into similar groups with respect to a similarity (or dissimilarity) measure. Among the clustering problems, specifically \textit{$k$-means} clustering has got much attention from the researchers. Despite the fact that $k$-means is a very well studied problem its status in the plane is still an open problem. In particular, it is unknown whether it admits a PTAS in the plane. The best known approximation bound in polynomial time is $9+\eps$. #R##N#In this paper, we consider the following variant of $k$-means. Given a set $C$ of points in $\mathcal{R}^d$ and a real $f > 0$, find a finite set $F$ of points in $\mathcal{R}^d$ that minimizes the quantity $f*|F|+\sum_{p\in C} \min_{q \in F} {||p-q||}^2$. For any fixed dimension $d$, we design a local search PTAS for this problem. We also give a "bi-criterion" local search algorithm for $k$-means which uses $(1+\eps)k$ centers and yields a solution whose cost is at most $(1+\eps)$ times the cost of an optimal $k$-means solution. The algorithm runs in polynomial time for any fixed dimension. #R##N#The contribution of this paper is two fold. On the one hand, we are being able to handle the square of distances in an elegant manner, which yields near optimal approximation bound. This leads us towards a better understanding of the $k$-means problem. On the other hand, our analysis of local search might also be useful for other geometric problems. This is important considering that very little is known about the local search method for geometric approximation.

Connected through hyperedge HE-1 (induced by source paper "k means few more steps yield constant approximation"):
  with members:
    - "a fast approximation scheme for low dimensional k means"
    - "a constant factor bi criteria approximation guarantee for k means"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "improved and simplified inapproximability for k means"
    - "the hardness of approximation of euclidean k means"
    - "scalable k means"

Connected through hyperedge HE-2 (induced by source paper "a constant fpt approximation algorithm for hard capacitated k means"):
  with members:
    - "constant factor fpt approximation for capacitated k median"
    - "greedy bi criteria approximations for k medians and k means"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation"
    - "improved and simplified inapproximability for k means"
    - "the hardness of approximation of euclidean k means"

Connected through hyperedge HE-3 (induced by source paper "one size fits all effectiveness of local search on structured data"):
  with members:
    - "the power of local search for clustering"
    - "tight analysis of a multiple swap heuristic for budgeted red blue median"
    - "local search yields a ptas for k means in doubling metrics"
    - "k center clustering under perturbation resilience"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "approximating k median via pseudo approximation"

Connected through hyperedge HE-4 (induced by source paper "a fast approximation scheme for low dimensional k means"):
  with members:
    - "tight analysis of a multiple swap heuristic for budgeted red blue median"
    - "new frameworks for offline and streaming coreset constructions"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"
    - "a bi criteria approximation algorithm for k means"
    - "the hardness of approximation of euclidean k means"
    - "an improved approximation for k median and positive correlation in budgeted optimization"

Connected through hyperedge HE-5 (induced by source paper "approximation schemes for clustering with outliers"):
  with members:
    - "a fast approximation scheme for low dimensional k means"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "the non uniform k center problem"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "improved and simplified inapproximability for k means"

Connected through hyperedge HE-6 (induced by source paper "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"):
  with members:
    - "the power of local search for clustering"
    - "local search yields a ptas for k means in doubling metrics"
    - "approximation algorithms for polynomial expansion and low density graphs"
    - "the hardness of approximation of euclidean k means"
    - "separating a voronoi diagram"
    - "approximating k median via pseudo approximation"
    - "improved spectral norm bounds for clustering"
    - "clustering under perturbation resilience"

Connected through hyperedge HE-7 (induced by source paper "local search yields a ptas for k means in doubling metrics"):
  with members:
    - "the power of local search for clustering"
    - "theoretical analysis of the k means algorithm a survey"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "the hardness of approximation of euclidean k means"
    - "the unreasonable success of local search geometric optimization"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "approximating k median via pseudo approximation"
    - "clustering with spectral norm and the k means algorithm"

Connected through hyperedge HE-8 (induced by source paper "the power of local search for clustering"):
  with members:
    - "the hardness of approximation of euclidean k means"
    - "separating a voronoi diagram"
    - "approximating k median via pseudo approximation"
    - "a unified framework for approximating and clustering data"
    - "parallel approximation algorithms for facility location problems"

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)