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: "optimality of the johnson lindenstrauss lemma"
  Abstract: For any integers $d, n \geq 2$ and $1/({\min\{n,d\}})^{0.4999} < \varepsilon<1$, we show the existence of a set of $n$ vectors $X\subset \mathbb{R}^d$ such that any embedding $f:X\rightarrow \mathbb{R}^m$ satisfying $$ \forall x,y\in X,\ (1-\varepsilon)\|x-y\|_2^2\le \|f(x)-f(y)\|_2^2 \le (1+\varepsilon)\|x-y\|_2^2 $$ must have $$ m = \Omega(\varepsilon^{-2} \lg n). $$ This lower bound matches the upper bound given by the Johnson-Lindenstrauss lemma [JL84]. Furthermore, our lower bound holds for nearly the full range of $\varepsilon$ of interest, since there is always an isometric embedding into dimension $\min\{d, n\}$ (either the identity map, or projection onto $\mathop{span}(X)$). #R##N#Previously such a lower bound was only known to hold against linear maps $f$, and not for such a wide range of parameters $\varepsilon, n, d$ [LN16]. The best previously known lower bound for general $f$ was $m = \Omega(\varepsilon^{-2}\lg n/\lg(1/\varepsilon))$ [Wel74, Lev83, Alo03], which is suboptimal for any $\varepsilon = o(1)$.

Connected through hyperedge HE-1 (induced by source paper "solving tall dense linear programs in nearly linear time"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "dynamic matrix inverse improved algorithms and matching conditional lower bounds"
    - "dynamic streaming spectral sparsification in nearly linear time and space"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "solving linear programs in the current matrix multiplication time"
    - "relative error tensor low rank approximation"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"

Connected through hyperedge HE-2 (induced by source paper "the gaussiansketch for almost relative error kernel distance"):
  with members:
    - "hashing based estimators for kernel density in high dimensions"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "locality sensitive hashing of curves"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "is input sparsity time possible for kernel low rank approximation"
    - "streaming kernel principal component analysis"
    - "fastmmd ensemble of circular discrepancy for efficient two sample test"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-3 (induced by source paper "faster johnson lindenstrauss transforms via kronecker products"):
  with members:
    - "oblivious sketching of high degree polynomial kernels"
    - "a practical randomized cp tensor decomposition"
    - "sketching for kronecker product regression and p splines"
    - "the restricted isometry property of subsampled fourier matrices"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-4 (induced by source paper "one pass sparsified gaussian mixtures"):
  with members:
    - "streaming low rank matrix approximation with an application to scientific simulation"
    - "practical sketching algorithms for low rank matrix approximation"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "lower bounds for oblivious subspace embeddings"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-5 (induced by source paper "performance of johnson lindenstrauss transform for k means and k medians clustering"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "deterministic feature selection for k means clustering"
    - "new constructions of rip matrices with fast multiplication and fewer rows"
    - "randomized dimensionality reduction for k means clustering"
    - "a sparse johnson lindenstrauss transform"
    - "random projections for k means clustering"

Connected through hyperedge HE-6 (induced by source paper "t sne cuda gpu accelerated t sne and its applications to modern data"):
  with members:
    - "an analysis of the t sne algorithm for data visualization"
    - "viser visual self regularization"
    - "billion scale similarity search with gpus"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "deep residual learning for image recognition"
    - "approximated and user steerable tsne for progressive visual analytics"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "nonlinear dimension reduction via outer bi lipschitz extensions"):
  with members:
    - "optimal terminal dimensionality reduction in euclidean space"
    - "approximate near neighbors for general symmetric norms"
    - "prioritized metric structures and embedding"
    - "sketching and embedding are equivalent for norms"
    - "constant factor approximation for balanced cut in the pie model"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "metric extension operators vertex sparsifiers and lipschitz extendability"
    - "ramsey type theorems for metric spaces with applications to online problems"

Connected through hyperedge HE-8 (induced by source paper "fully understanding the hashing trick"):
  with members:
    - "hashing for statistics over k partitions"
    - "simple tabulation fast expanders double tabulation and high independence"
    - "random projections for linear support vector machines"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "a sparse johnson lindenstrauss transform"
    - "sparser johnson lindenstrauss transforms"
    - "feature hashing for large scale multitask learning"

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.IT(Information Theory)