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: "approximation and streaming algorithms for projective clustering via random projections"
  Abstract: Let $P$ be a set of $n$ points in $\mathbb{R}^d$. In the projective clustering problem, given $k, q$ and norm $\rho \in [1,\infty]$, we have to compute a set $\mathcal{F}$ of $k$ $q$-dimensional flats such that $(\sum_{p\in P}d(p, \mathcal{F})^\rho)^{1/\rho}$ is minimized; here $d(p, \mathcal{F})$ represents the (Euclidean) distance of $p$ to the closest flat in $\mathcal{F}$. We let $f_k^q(P,\rho)$ denote the minimal value and interpret $f_k^q(P,\infty)$ to be $\max_{r\in P}d(r, \mathcal{F})$. When $\rho=1,2$ and $\infty$ and $q=0$, the problem corresponds to the $k$-median, $k$-mean and the $k$-center clustering problems respectively. #R##N#For every $0 < \epsilon < 1$, $S\subset P$ and $\rho \ge 1$, we show that the orthogonal projection of $P$ onto a randomly chosen flat of dimension $O(((q+1)^2\log(1/\epsilon)/\epsilon^3) \log n)$ will $\epsilon$-approximate $f_1^q(S,\rho)$. This result combines the concepts of geometric coresets and subspace embeddings based on the Johnson-Lindenstrauss Lemma. As a consequence, an orthogonal projection of $P$ to an $O(((q+1)^2 \log ((q+1)/\epsilon)/\epsilon^3) \log n)$ dimensional randomly chosen subspace $\epsilon$-approximates projective clusterings for every $k$ and $\rho$ simultaneously. Note that the dimension of this subspace is independent of the number of clusters~$k$. #R##N#Using this dimension reduction result, we obtain new approximation and streaming algorithms for projective clustering problems. For example, given a stream of $n$ points, we show how to compute an $\epsilon$-approximate projective clustering for every $k$ and $\rho$ simultaneously using only $O((n+d)((q+1)^2\log ((q+1)/\epsilon))/\epsilon^3 \log n)$ space. Compared to standard streaming algorithms with $\Omega(kd)$ space requirement, our approach is a significant improvement when the number of input points and their dimensions are of the same order of magnitude.

Connected through hyperedge HE-1 (induced by source paper "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers"):
  with members:
    - "greedy strategy works for k center clustering with outliers and coreset construction"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "the non uniform k center problem"
    - "adversarial machine learning at scale"
    - "recovering the optimal solution by dual random projection"
    - "random projections for linear support vector machines"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-2 (induced by source paper "coresets for gaussian mixture models of any shape"):
  with members:
    - "map inference via block coordinate frank wolfe algorithm"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "minimizing sum of non convex but piecewise log lipschitz functions using coresets"
    - "learning mixture of gaussians with streaming data"
    - "on the k means median cost function"
    - "secure search on the cloud via coresets and sketches"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "new frameworks for offline and streaming coreset constructions"

Connected through hyperedge HE-3 (induced by source paper "minimum enclosing ball revisited stability sub linear time algorithms and extension"):
  with members:
    - "beyond worst case analysis"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "distributed partial clustering"
    - "coresets and sketches"
    - "locating a small cluster privately"
    - "the non uniform k center problem"
    - "k center clustering under perturbation resilience"
    - "optimization algorithms for faster computational geometry"

Connected through hyperedge HE-4 (induced by source paper "minimum enclosing ball revisited stability and sub linear time algorithms"):
  with members:
    - "beyond worst case analysis"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "coresets and sketches"
    - "locating a small cluster privately"
    - "k center clustering under perturbation resilience"
    - "optimization algorithms for faster computational geometry"
    - "clustering under perturbation resilience"
    - "clustering with spectral norm and the k means algorithm"

Connected through hyperedge HE-5 (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"
    - "on variants of k means clustering"
    - "k center clustering under perturbation resilience"
    - "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-6 (induced by source paper "persistent homology for low complexity models"):
  with members:
    - "isometric sketching of any set via the restricted isometry property"
    - "practical and optimal lsh for angular distance"
    - "the simplex tree an efficient data structure for general simplicial complexes"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "local doubling dimension of point sets"
    - "a unified framework for linear dimensionality reduction in l1"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-7 (induced by source paper "polynomial sized topological approximations using the permutahedron"):
  with members:
    - "linear size approximations to the vietoris rips filtration"
    - "computing topological persistence for simplicial maps"

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)