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: "sparse approximation via generating point sets"
  Abstract: $ \newcommand{\kalg}{{k_{\mathrm{alg}}}} #R##N#\newcommand{\kopt}{{k_{\mathrm{opt}}}} #R##N#\newcommand{\algset}{{T}} \renewcommand{\Re}{\mathbb{R}} #R##N#\newcommand{\eps}{\varepsilon} \newcommand{\pth}[2][\!]{#1\left({#2}\right)} \newcommand{\npoints}{n} \newcommand{\ballD}{\mathsf{b}} \newcommand{\dataset}{{P}} $ For a set $\dataset$ of $\npoints$ points in the unit ball $\ballD \subseteq \Re^d$, consider the problem of finding a small subset $\algset \subseteq \dataset$ such that its convex-hull $\eps$-approximates the convex-hull of the original set. We present an efficient algorithm to compute such a $\eps'$-approximation of size $\kalg$, where $\eps'$ is function of $\eps$, and $\kalg$ is a function of the minimum size $\kopt$ of such an $\eps$-approximation. Surprisingly, there is no dependency on the dimension $d$ in both bounds. Furthermore, every point of $\dataset$ can be $\eps$-approximated by a convex-combination of points of $\algset$ that is $O(1/\eps^2)$-sparse. #R##N#Our result can be viewed as a method for sparse, convex autoencoding: approximately representing the data in a compact way using sparse combinations of a small subset $\algset$ of the original data. The new algorithm can be kernelized, and it preserves sparsity in the original input.

Connected through hyperedge HE-1 (induced by source paper "approximate guarantees for dictionary learning"):
  with members:
    - "towards learning sparsely used dictionaries with arbitrary supports"
    - "on the computational intractability of exact and approximate dictionary learning"
    - "variable selection is hard"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "hypercontractivity sum of squares proofs and their applications"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-2 (induced by source paper "online convex matrix factorization with representative regions"):
  with members:
    - "a ranking model motivated by nonnegative matrix factorization with applications to tennis tournaments"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"
    - "simultaneous codeword optimization simco for dictionary update and learning"
    - "convex sparse matrix factorizations"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "robust vertex enumeration for convex hulls in high dimensions"):
  with members:
    - "on some provably correct cases of variational inference for topic models"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "a characterization theorem and an algorithm for a convex hull problem"
    - "a spectral algorithm for latent dirichlet allocation"
    - "learning topic models going beyond svd"
    - "exact recovery of sparsely used dictionaries"
    - "computing a nonnegative matrix factorization provably"

Connected through hyperedge HE-4 (induced by source paper "spherical triangle algorithm a fast oracle for convex hull membership queries"):
  with members:
    - "robust vertex enumeration for convex hulls in high dimensions"
    - "an algorithmic separating hyperplane theorem and its applications"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "a characterization theorem and an algorithm for a convex hull problem"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "tight bounds for approximate carath eodory and beyond"):
  with members:
    - "provable submodular minimization using wolfe s algorithm"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "convex analysis and optimization with submodular functions a tutorial"
    - "sequential and parallel algorithms for mixed packing and covering"
    - "on the number of iterations for dantzig wolfe optimization and packing covering approximation algorithms"

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)