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: "random projections for linear support vector machines"
  Abstract: Let X be a data matrix of rank \rho, whose rows represent n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input matrix X. We prove that, with high probability, the margin and minimum enclosing ball in the feature space are preserved to within \epsilon-relative error, ensuring comparable generalization as in the original space in the case of classification. For regression, we show that the margin is preserved to \epsilon-relative error with high probability. We present extensive experiments with real and synthetic data to support our theory.

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"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "recovering the optimal solution by dual random projection"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-2 (induced by source paper "stable sparse subspace embedding for dimensionality reduction"):
  with members:
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "recovering the optimal solution by dual random projection"
    - "low rank approximation and regression in input sparsity time"
    - "random projections for k means clustering"
    - "sparser johnson lindenstrauss transforms"
    - "bounded independence fools degree 2 threshold functions"

Connected through hyperedge HE-3 (induced by source paper "fully understanding the hashing trick"):
  with members:
    - "optimality of the johnson lindenstrauss lemma"
    - "hashing for statistics over k partitions"
    - "simple tabulation fast expanders double tabulation and high independence"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "a sparse johnson lindenstrauss transform"
    - "sparser johnson lindenstrauss transforms"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-4 (induced by source paper "how to fake multiply by a gaussian matrix"):
  with members:
    - "compressed nonnegative matrix factorization is fast and accurate"
    - "sketching as a tool for numerical linear algebra"
    - "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices"
    - "fastfood approximate kernel expansions in loglinear time"
    - "low rank approximation and regression in input sparsity time"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-5 (induced by source paper "large scale strategic games and adversarial machine learning"):
  with members:
    - "a learning based approach to reactive security"

Connected through hyperedge HE-6 (induced by source paper "lower bounds for oblivious subspace embeddings"):
  with members:
    - "low rank approximation and regression in input sparsity time"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "almost optimal explicit johnson lindenstrauss transformations"

Connected through hyperedge HE-7 (induced by source paper "low rank approximation and regression in input sparsity time"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "quantile regression for large scale applications"
    - "subspace embeddings and ell_p regression using exponential random variables"
    - "lower bounds for oblivious subspace embeddings"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "a matrix hyperbolic cosine algorithm and applications"

Connected through hyperedge HE-8 (induced by source paper "recovering the optimal solution by dual random projection"):
  with members:
    - "a simple algorithm for semi supervised learning with improved generalization error bound"
    - "is margin preserved after random projection"
    - "rademacher chaos random eulerian graphs and the sparse johnson lindenstrauss transform"
    - "relative error cur matrix decompositions"

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.LG(Machine Learning)