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: "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
  Abstract: For any $n>1$ and $0<\varepsilon<1/2$, we show the existence of an $n^{O(1)}$-point subset $X$ of $\mathbb{R}^n$ such that any linear map from $(X,\ell_2)$ to $\ell_2^m$ with distortion at most $1+\varepsilon$ must have $m = \Omega(\min\{n, \varepsilon^{-2}\log n\})$. Our lower bound matches the upper bounds provided by the identity matrix and the Johnson-Lindenstrauss lemma, improving the previous lower bound of Alon by a $\log(1/\varepsilon)$ factor.

Connected through hyperedge HE-1 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications"):
  with members:
    - "phase retrieval under a generative prior"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "why are deep nets reversible a simple theory with implications for training"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction with subgaussian matrices a unified theory"

Connected through hyperedge HE-2 (induced by source paper "similarity problems in high dimensions"):
  with members:
    - "approximate furthest neighbor with application to annulus query"
    - "distributed pcp theorems for hardness of approximation in p"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "set similarity search beyond minhash"
    - "distance sensitive bloom filters without false negatives"

Connected through hyperedge HE-3 (induced by source paper "nonlinear dimension reduction via outer bi lipschitz extensions"):
  with members:
    - "optimal terminal dimensionality reduction in euclidean space"
    - "optimality of the johnson lindenstrauss lemma"
    - "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"
    - "metric extension operators vertex sparsifiers and lipschitz extendability"
    - "ramsey type theorems for metric spaces with applications to online problems"

Connected through hyperedge HE-4 (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"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "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-5 (induced by source paper "bolt accelerated data mining with fast vector compression"):
  with members:
    - "polysemous codes"
    - "structured adaptive and random spinners for fast machine learning computations"
    - "orthogonal random features"
    - "loss aware binarization of deep networks"
    - "generalized residual vector quantization for large scale data"
    - "learning both weights and connections for efficient neural networks"
    - "acdc a structured efficient linear layer"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-6 (induced by source paper "machine learning friendly set version of johnson lindenstrauss lemma"):
  with members:
    - "optimality of the johnson lindenstrauss lemma"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "compressive spectral clustering"
    - "random sampling of bandlimited signals on graphs"
    - "computational feasibility of clustering under clusterability assumptions"
    - "low rank approximation and regression in input sparsity time"
    - "large scale spectral clustering using resistance distance and spielman teng solvers"
    - "clustering under perturbation resilience"

Connected through hyperedge HE-7 (induced by source paper "embeddings of schatten norms with applications to data streams"):
  with members:
    - "sketches for matrix norms faster smaller and more general"
    - "spectrum estimation from samples"
    - "on approximating functions of the singular values in a stream"
    - "streaming symmetric norms via measure concentration"
    - "principal component analysis and higher correlations for distributed data"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "a derandomized sparse johnson lindenstrauss transform"

Connected through hyperedge HE-8 (induced by source paper "fast nearest neighbor preserving embeddings"):
  with members:
    - "a sparse johnson lindenstrauss transform"
    - "sparser johnson lindenstrauss transforms"

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)