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: "lower bounds for oblivious subspace embeddings"
  Abstract: An oblivious subspace embedding (OSE) for some eps, delta in (0,1/3) and d <= m <= n is a distribution D over R^{m x n} such that for any linear subspace W of R^n of dimension d, #R##N#Pr_{Pi ~ D}(for all x in W, (1-eps) |x|_2  = 1 - delta. #R##N#We prove that any OSE with delta < 1/3 must have m = Omega((d + log(1/delta))/eps^2), which is optimal. Furthermore, if every Pi in the support of D is sparse, having at most s non-zero entries per column, then we show tradeoff lower bounds between m and s.

Connected through hyperedge HE-1 (induced by source paper "streaming low rank matrix approximation with an application to scientific simulation"):
  with members:
    - "low rank tucker approximation of a tensor from streaming data"
    - "practical sketching algorithms for low rank matrix approximation"
    - "randomized single view algorithms for low rank matrix approximation"
    - "fast and space optimal low rank factorization in the streaming model with application in differential privacy"
    - "parallel tensor compression for large scale scientific data"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal principal component analysis in distributed and streaming models"

Connected through hyperedge HE-2 (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"
    - "optimality of the johnson lindenstrauss lemma"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-3 (induced by source paper "tight bounds for ell_p oblivious subspace embeddings"):
  with members:
    - "uniform sampling for matrix approximation"
    - "quantile regression for large scale applications"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "the fast cauchy transform and faster robust linear regression"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-4 (induced by source paper "practical sketching algorithms for low rank matrix approximation"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "randomized single view algorithms for low rank matrix approximation"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "optimal principal component analysis in distributed and streaming models"
    - "optimal approximate matrix product in terms of stable rank"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"

Connected through hyperedge HE-5 (induced by source paper "near optimal sketching of low rank tensor regression"):
  with members:
    - "deep multi task representation learning a tensor factorisation approach"
    - "sketching as a tool for numerical linear algebra"
    - "low rank approximation and regression in input sparsity time"
    - "fast approximation of matrix coherence and statistical leverage"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-6 (induced by source paper "optimal approximate matrix product in terms of stable rank"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "improved distributed principal component analysis"
    - "uniform sampling for matrix approximation"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "low rank approximation and regression in input sparsity time"
    - "new probabilistic bounds on eigenvalues and eigenvectors of random kernel matrices"
    - "iterative row sampling"

Connected through hyperedge HE-7 (induced by source paper "fast spectral low rank matrix approximation"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "sketching as a tool for numerical linear algebra"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "fast matrix rank algorithms and applications"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "using a non commutative bernstein bound to approximate some matrix algorithms in the spectral norm"

Connected through hyperedge HE-8 (induced by source paper "sketching as a tool for numerical linear algebra"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "optimal approximate matrix product in terms of stable rank"
    - "optimal cur matrix decompositions"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "uniform sampling for matrix approximation"
    - "single pass spectral sparsification in dynamic streams"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "subspace embeddings and ell_p regression using exponential random variables"

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.DM(Discrete Mathematics)