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 "a derandomized sparse johnson lindenstrauss transform"):
  Abstract: Recent work of [Dasgupta-Kumar-Sarlos, STOC 2010] gave a sparse Johnson-Lindenstrauss transform and left as a main open question whether their construction could be efficiently derandomized. We answer their question affirmatively by giving an alternative proof of their result requiring only bounded independence hash functions. Furthermore, the sparsity bound obtained in our proof is improved. The main ingredient in our proof is a spectral moment bound for quadratic forms that was recently used in [Diakonikolas-Kane-Nelson, FOCS 2010].

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 "almost optimal explicit johnson lindenstrauss transformations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lower bounds for oblivious subspace embeddings")
  Member "new and improved johnson lindenstrauss embeddings via the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding the mode of a kernel density estimate")
  Member "pseudorandom generators for polynomial threshold functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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)