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: "almost optimal explicit johnson lindenstrauss transformations"
  Abstract: The Johnson-Lindenstrauss lemma is a fundamental result in probability with several applications in the design and analysis of algorithms in high dimensional geometry. Most known constructions of linear embeddings that satisfy the Johnson-Lindenstrauss property involve randomness. We address the question of explicitly constructing such embedding families and provide a construction with an almost optimal use of randomness: we use O(log(n/delta)log(log(n/delta)/epsilon)) random bits for embedding n dimensions to O(log(1/delta)/epsilon^2) dimensions with error probability at most delta, and distortion at most epsilon. #R##N#In particular, for delta = 1/poly(n) and fixed epsilon, we use O(log n loglog n) random bits. Previous constructions required at least O(log^2 n) random bits to get polynomially small error.

Connected through hyperedge HE-1 (induced by source paper "lower bounds for oblivious subspace embeddings"):
  with members:
    - "random projections for linear support vector machines"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "sparser johnson lindenstrauss transforms"):
  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"
    - "a sparse johnson lindenstrauss transform"
    - "fast moment estimation in data streams in optimal space"
    - "a derandomized sparse johnson lindenstrauss transform"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-3 (induced by source paper "a derandomized sparse johnson lindenstrauss transform"):
  with members:
    - "a sparse johnson lindenstrauss transform"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"
    - "pseudorandom generators for polynomial threshold functions"
    - "bounded independence fools degree 2 threshold functions"

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.DS(Data Structures and Algorithms)