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 "lower bounds on time space trade offs for approximate near neighbors"):
  Abstract: We show tight lower bounds for the entire trade-off between space and query time for the Approximate Near Neighbor search problem. Our lower bounds hold in a restricted model of computation, which captures all hashing-based approaches. In articular, our lower bound matches the upper bound recently shown in [Laarhoven 2015] for the random instance on a Euclidean sphere (which we show in fact extends to the entire space $\mathbb{R}^d$ using the techniques from [Andoni, Razenshteyn 2015]). #R##N#We also show tight, unconditional cell-probe lower bounds for one and two probes, improving upon the best known bounds from [Panigrahy, Talwar, Wieder 2010]. In particular, this is the first space lower bound (for any static data structure) for two probes which is not polynomially smaller than for one probe. To show the result for two probes, we establish and exploit a connection to locally-decodable codes.

Includes members:
  Member "randomized approximate nearest neighbor search with limited adaptivity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hardness of approximate nearest neighbor search")
  Member "simple average case lower bounds for approximate near neighbor from isoperimetric inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "randomized approximate nearest neighbor search with limited adaptivity")
  Member "exploiting the structure stochastic gradient methods using raw clusters":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "data sketching for faster training of machine learning models")
  Member "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a faster algorithm enumerating relevant features over finite fields")
  Member "scalability and total recall with fast coveringlsh":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal las vegas approximate near neighbors in ell_p")
  Member "learning to hash for indexing big data a survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "random vlad based deep hashing for efficient image retrieval")
  Member "a faster subquadratic algorithm for finding outlier correlations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "subsets and supermajorities unifying hashing based set similarity search")
  Member "tight lower bounds for data dependent locality sensitive hashing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "algorithms for similarity search and pseudorandomness")

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)