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: "approximate nearest neighbor search in ell_p"
  Abstract: We present a new locality sensitive hashing (LSH) algorithm for $c$-approximate nearest neighbor search in $\ell_p$ with $1<p<2$. For a database of $n$ points in $\ell_p$, we achieve $O(dn^{\rho})$ query time and $O(dn+n^{1+\rho})$ space, where $\rho \le O((\ln c)^2/c^p)$. This improves upon the previous best upper bound $\rho\le 1/c$ by Datar et al. (SOCG 2004), and is close to the lower bound $\rho \ge 1/c^p$ by O'Donnell, Wu and Zhou (ITCS 2011). The proof is a simple generalization of the LSH scheme for $\ell_2$ by Andoni and Indyk (FOCS 2006).

Connected through hyperedge HE-1 (induced by source paper "light spanners for high dimensional norms via stochastic decompositions"):
  with members:
    - "minor free graphs have light spanners"
    - "the greedy spanner is existentially optimal"
    - "a light metric spanner"
    - "cops robbers and threatening skeletons padded decomposition for minor free graphs"
    - "ramsey partitions and proximity data structures"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-2 (induced by source paper "a framework for similarity search with space time tradeoffs using locality sensitive filtering"):
  with members:
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "lower bounds on time space trade offs for approximate near neighbors"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "practical and optimal lsh for angular distance"
    - "optimal data dependent hashing for approximate near neighbors"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "analysis of boolean functions"
    - "hashing for similarity search a survey"

Connected through hyperedge HE-3 (induced by source paper "dimension reduction techniques for ell_p 1 le p infty with applications"):
  with members:
    - "fast precise and dynamic distance queries"
    - "a nonlinear approach to dimension reduction"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "entropy based nearest neighbor search in high dimensions"
    - "minimum enclosing polytope in high dimensions"

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)