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: "a fast implementation of near neighbors queries for frechet distance gis cup"
  Abstract: This paper describes an implementation of fast near-neighbours queries (also known as range searching) with respect to the Frechet distance. The algorithm is designed to be efficient on practical data such as GPS trajectories. Our approach is to use a quadtree data structure to enumerate all curves in the database that have similar start and endpoints as the query curve. On these curves we run positive and negative filters to narrow the set of potential results. Only for those trajectories where these heuristics fail, we compute the Frechet distance exactly, by running a novel recursive variant of the classic free-space diagram algorithm.   Our implementation is among the top 3 submissions to the ACM SIGSPATIAL GIS Cup 2017 (more specific standings will be available on the competition's website).

Connected through hyperedge HE-1 (induced by source paper "random projections and sampling algorithms for clustering of high dimensional polygonal curves"):
  with members:
    - "on the hardness of computing an average curve"
    - "the one way communication complexity of dynamic time warping distance"
    - "walking the dog fast in practice algorithm engineering of the fr echet distance"
    - "beyond worst case analysis"
    - "on coresets for logistic regression"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "clustering is difficult only when it does not matter"

Connected through hyperedge HE-2 (induced by source paper "sublinear data structures for short fr echet queries"):
  with members:
    - "the one way communication complexity of dynamic time warping distance"
    - "approximate nearest neighbor for curves simple efficient and deterministic"
    - "on optimal polyline simplification using the hausdorff and fr echet distance"
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "coresets and sketches"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-3 (induced by source paper "walking the dog fast in practice algorithm engineering of the fr echet distance"):
  with members:
    - "approximating k ell center clustering for curves"
    - "fresh fr echet similarity with hashing"
    - "four soviets walk the dog improved bounds for computing the frechet distance"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "intermittent map matching with the discrete frechet distance"

Connected through hyperedge HE-4 (induced by source paper "the vc dimension of metric balls under fr echet and hausdorff distances"):
  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"
    - "a new upper bound for the vc dimension of visibility regions"

Connected through hyperedge HE-5 (induced by source paper "fresh fr echet similarity with hashing"):
  with members:
    - "approximate nearest neighbor search in high dimensions"
    - "locality sensitive hashing of curves"
    - "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"

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)