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: "jaywalking your dog computing the fr echet distance with shortcuts"
  Abstract: The similarity of two polygonal curves can be measured using the Fr\'echet distance. We introduce the notion of a more robust Fr\'echet distance, where one is allowed to shortcut between vertices of one of the curves. This is a natural approach for handling noise, in particular batched outliers. We compute a (3+\eps)-approximation to the minimum Fr\'echet distance over all possible such shortcuts, in near linear time, if the curve is c-packed and the number of shortcuts is either small or unbounded. #R##N#To facilitate the new algorithm we develop several new tools: #R##N#(A) A data structure for preprocessing a curve (not necessarily c-packed) that supports (1+\eps)-approximate Fr\'echet distance queries between a subcurve (of the original curve) and a line segment. #R##N#(B) A near linear time algorithm that computes a permutation of the vertices of a curve, such that any prefix of 2k-1 vertices of this permutation, form an optimal approximation (up to a constant factor) to the original curve compared to any polygonal curve with k vertices, for any k > 0. #R##N#(C) A data structure for preprocessing a curve that supports approximate Fr\'echet distance queries between a subcurve and query polygonal curve. The query time depends quadratically on the complexity of the query curve, and only (roughly) logarithmically on the complexity of the original curve. #R##N#To our knowledge, these are the first data structures to support these kind of queries efficiently.

Connected through hyperedge HE-1 (induced by source paper "clustering time series under the fr echet distance"):
  with members:
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "how good is the chord algorithm"
    - "on the impossibility of dimension reduction for doubling subsets of ell_p p 2"
    - "approximating k median via pseudo approximation"
    - "four soviets walk the dog with an application to alt s conjecture"
    - "a unified framework for approximating and clustering data"
    - "approximating the fr echet distance for realistic curves in near linear time"

Connected through hyperedge HE-2 (induced by source paper "on the chain pair simplification problem"):
  with members:
    - "the discrete frechet distance with shortcuts via approximate distance counting and selection techniques"
    - "computing the fr e chet distance with shortcuts is np hard"
    - "four soviets walk the dog with an application to alt s conjecture"
    - "locally correct frechet matchings"

Connected through hyperedge HE-3 (induced by source paper "computing the discrete fr echet distance in subquadratic time"):
  with members:
    - "four soviets walk the dog with an application to alt s conjecture"
    - "unified compression based acceleration of edit distance computation"

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)