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: "spectral approaches to nearest neighbor search"
  Abstract: We study spectral algorithms for the high-dimensional Nearest Neighbor Search problem (NNS). In particular, we consider a semi-random setting where a dataset $P$ in $\mathbb{R}^d$ is chosen arbitrarily from an unknown subspace of low dimension $k\ll d$, and then perturbed by fully $d$-dimensional Gaussian noise. We design spectral NNS algorithms whose query time depends polynomially on $d$ and $\log n$ (where $n=|P|$) for large ranges of $k$, $d$ and $n$. Our algorithms use a repeated computation of the top PCA vector/subspace, and are effective even when the random-noise magnitude is {\em much larger} than the interpoint distances in $P$. Our motivation is that in practice, a number of spectral NNS algorithms outperform the random-projection methods that seem otherwise theoretically optimal on worst case datasets. In this paper we aim to provide theoretical justification for this disparity.

Connected through hyperedge HE-1 (induced by source paper "learning space partitions for nearest neighbor search"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "approximate nearest neighbor search in high dimensions"
    - "competitive caching with machine learned advice"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "the case for learned index structures"
    - "billion scale similarity search with gpus"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-2 (induced by source paper "learning sublinear time indexing for nearest neighbor search"):
  with members:
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "approximate nearest neighbor search in high dimensions"
    - "competitive caching with machine learned advice"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "billion scale similarity search with gpus"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "a deep learning approach to structured signal recovery"
    - "learning to hash for indexing big data a survey"

Connected through hyperedge HE-3 (induced by source paper "subspace approximation for approximate nearest neighbor search in nlp"):
  with members:
    - "learning to compose neural networks for question answering"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "ask me anything dynamic memory networks for natural language processing"
    - "lexicon infused phrase embeddings for named entity resolution"

Connected through hyperedge HE-4 (induced by source paper "optimal data dependent hashing for approximate near neighbors"):
  with members:
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "hashing for similarity search a survey"
    - "beyond locality sensitive hashing"
    - "bucketing coding and information theory for the statistical high dimensional nearest neighbor problem"
    - "lower bounds on near neighbor search via metric expansion"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "lower bounds on locality sensitive hashing"
    - "approximate graph coloring by semidefinite programming"

Connected through hyperedge HE-5 (induced by source paper "binary coding in stream"):
  with members:
    - "relative errors for deterministic low rank matrix approximations"
    - "the supervised ibp neighbourhood preserving infinite latent feature models"
    - "simple and deterministic matrix sketching"
    - "approximate nearest neighbor search for low dimensional queries"

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)