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: "fast cross polytope locality sensitive hashing"
  Abstract: We provide a variant of cross-polytope locality sensitive hashing with respect to angular distance which is provably optimal in asymptotic sensitivity and enjoys $\mathcal{O}(d \ln d )$ hash computation time. Building on a recent result (by Andoni, Indyk, Laarhoven, Razenshteyn, Schmidt, 2015), we show that optimal asymptotic sensitivity for cross-polytope LSH is retained even when the dense Gaussian matrix is replaced by a fast Johnson-Lindenstrauss transform followed by discrete pseudo-rotation, reducing the hash computation time from $\mathcal{O}(d^2)$ to $\mathcal{O}(d \ln d )$. Moreover, our scheme achieves the optimal rate of convergence for sensitivity. By incorporating a low-randomness Johnson-Lindenstrauss transform, our scheme can be modified to require only $\mathcal{O}(\ln^9(d))$ random bits

Connected through hyperedge HE-1 (induced by source paper "algorithms for similarity search and pseudorandomness"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast similarity sketching"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "optimal densification for fast and accurate minwise hashing"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "scalable and robust set similarity join"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"

Connected through hyperedge HE-2 (induced by source paper "polytopes lattices and spherical codes for the nearest neighbor problem"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "hypercube lsh for approximate near neighbors"
    - "practical and optimal lsh for angular distance"
    - "bucketing coding and information theory for the statistical high dimensional nearest neighbor problem"

Connected through hyperedge HE-3 (induced by source paper "local density estimation in high dimensions"):
  with members:
    - "the role of context types and dimensionality in learning word embeddings"
    - "node2vec scalable feature learning for networks"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (induced by source paper "fast locality sensitive hashing frameworks for approximate near neighbor search"):
  with members:
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast similarity sketching"
    - "optimal densification for fast and accurate minwise hashing"
    - "scalable and robust set similarity join"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "practical and optimal lsh for angular distance"
    - "hashing for similarity search a survey"

Connected through hyperedge HE-5 (induced by source paper "fast locality sensitive hashing for approximate near neighbor search"):
  with members:
    - "fast similarity sketching"
    - "scalable and robust set similarity join"
    - "optimal hashing based time space trade offs for approximate near neighbors"

Connected through hyperedge HE-6 (induced by source paper "generic lsh families for the angular distance based on johnson lindenstrauss projections and feature hashing lsh"):
  with members:
    - "practical and optimal lsh for angular distance"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-7 (induced by source paper "hypercube lsh for approximate near neighbors"):
  with members:
    - "bucketing coding and information theory for the statistical high dimensional nearest neighbor problem"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"

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)