human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "a sparse johnson lindenstrauss transform":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers")
  Member "a derandomized sparse johnson lindenstrauss transform":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed learning with sublinear communication")
  Member "pseudorandom generators for polynomial threshold functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "degree d chow parameters robustly determine degree d ptfs and algorithmic applications")
  Member "bounded independence fools degree 2 threshold functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stable sparse subspace embedding for dimensionality reduction")

Question: Please tell me which class the source paper 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)