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: "locality sensitive hashing of curves"
  Abstract: We study data structures for storing a set of polygonal curves in R^d such that, given a query curve, we can efficiently retrieve similar curves from the set, where similarity is measured using the discrete Frechet distance or the dynamic time warping distance. To this end we devise the first locality-sensitive hashing schemes for these distance measures. A major challenge is posed by the fact that these distance measures internally optimize the alignment between the curves. We give solutions for different types of alignments including constrained and unconstrained versions. For unconstrained alignments, we improve over a result by Indyk [SoCG 2002] for short curves. Let n be the number of input curves and let m be the maximum complexity of a curve in the input. In the particular case where m  0, our solutions imply an approximate near-neighbor data structure for the discrete Frechet distance that uses space in O(n^(1+a) log n) and achieves query time in O(n^a log^2 n) and constant approximation factor. Furthermore, our solutions provide a trade-off between approximation quality and computational performance: for any parameter k in [m], we can give a data structure that uses space in O(2^(2k) m^(k-1) n log n + nm), answers queries in O( 2^(2k) m^(k) log n) time and achieves approximation factor in O(m/k).

Connected through hyperedge HE-1 (induced by source paper "the gaussiansketch for almost relative error kernel distance"):
  with members:
    - "hashing based estimators for kernel density in high dimensions"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "is input sparsity time possible for kernel low rank approximation"
    - "optimality of the johnson lindenstrauss lemma"
    - "streaming kernel principal component analysis"
    - "fastmmd ensemble of circular discrepancy for efficient two sample test"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-2 (induced by source paper "dynamic time warping in strongly subquadratic time algorithms for the low distance regime and approximate evaluation"):
  with members:
    - "the one way communication complexity of dynamic time warping distance"
    - "approximating edit distance within constant factor in truly sub quadratic time"
    - "on estimating edit distance alignment dimension reduction and embeddings"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "streaming algorithms for computing edit distance without exploiting suffix trees"
    - "dynamic time warping and geometric edit distance breaking the quadratic barrier"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "approximating dynamic time warping and edit distance for a pair of point sequences"

Connected through hyperedge HE-3 (induced by source paper "the one way communication complexity of dynamic time warping distance"):
  with members:
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "edit distance sketching streaming and document exchange"
    - "dynamic time warping and geometric edit distance breaking the quadratic barrier"
    - "efficient deterministic single round document exchange for edit distance"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "approximating dynamic time warping and edit distance for a pair of point sequences"
    - "sparsedtw a novel approach to speed up dynamic time warping"
    - "voice recognition algorithms using mel frequency cepstral coefficient mfcc and dynamic time warping dtw techniques"

Connected through hyperedge HE-4 (induced by source paper "approximate nearest neighbor for curves simple efficient and deterministic"):
  with members:
    - "sublinear data structures for short fr echet queries"
    - "nonlinear dimension reduction via outer bi lipschitz extensions"
    - "optimal terminal dimensionality reduction in euclidean space"
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "faster retrieval with a two pass dynamic time warping lower bound"

Connected through hyperedge HE-5 (induced by source paper "efficient nearest neighbor query and clustering of planar curves"):
  with members:
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "spatio temporal analysis of team sports"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "computing the discrete fr echet distance in subquadratic time"

Connected through hyperedge HE-6 (induced by source paper "fresh fr echet similarity with hashing"):
  with members:
    - "approximate nearest neighbor search in high dimensions"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast locality sensitive hashing frameworks for approximate near neighbor search"
    - "spatio temporal analysis of team sports"
    - "parameter free locality sensitive hashing for spherical range reporting"

Connected through hyperedge HE-7 (induced by source paper "adaptive mapreduce similarity joins"):
  with members:
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "scalable and robust set similarity join"
    - "experimental evaluation of multi round matrix multiplication on mapreduce"
    - "i o efficient similarity join"
    - "communication steps for parallel query processing"
    - "space round tradeoffs for mapreduce computations"

Connected through hyperedge HE-8 (induced by source paper "approximate nearest neighbors in the space of persistence diagrams"):
  with members:
    - "learning simplicial complexes from persistence diagrams"
    - "geometry helps to compare persistence diagrams"

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.CG(Computational Geometry)