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: "locally correct frechet matchings"
  Abstract: The Frechet distance is a metric to compare two curves, which is based on monotonous matchings between these curves. We call a matching that results in the Frechet distance a Frechet matching. There are often many different Frechet matchings and not all of these capture the similarity between the curves well. We propose to restrict the set of Frechet matchings to "natural" matchings and to this end introduce locally correct Frechet matchings. We prove that at least one such matching exists for two polygonal curves and give an O(N^3 log N) algorithm to compute it, where N is the total number of edges in both curves. We also present an O(N^2) algorithm to compute a locally correct discrete Frechet matching.

Connected through hyperedge HE-1 (induced by source paper "four soviets walk the dog improved bounds for computing the frechet distance"):
  with members:
    - "computing the frechet distance with a retractable leash"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "threesomes degenerates and love triangles"
    - "how to walk your dog in the mountains with no magic leash"
    - "on range searching with semialgebraic sets ii"
    - "can we compute the similarity between surfaces"

Connected through hyperedge HE-2 (induced by source paper "discretized approaches to schematization"):
  with members:
    - "solving the maximum weight connected subgraph problem to optimality"
    - "intermittent map matching with the discrete frechet distance"
    - "simple curve embedding"
    - "map matching with simplicity constraints"

Connected through hyperedge HE-3 (induced by source paper "computing the frechet distance with a retractable leash"):
  with members:
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "four soviets walk the dog with an application to alt s conjecture"
    - "geodesic fr echet distance inside a simple polygon"

Connected through hyperedge HE-4 (induced by source paper "approximating the integral fr echet distance"):
  with members:
    - "similarity of polygonal curves in the presence of outliers"

Connected through hyperedge HE-5 (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"
    - "jaywalking your dog computing the fr echet distance with shortcuts"

Connected through hyperedge HE-6 (induced by source paper "applied similarity problems using frechet distance"):
  with members:
    - "four soviets walk the dog with an application to alt s conjecture"
    - "similarity of polygonal curves in the presence of outliers"
    - "hardness results on curve point set matching with frechet distance"
    - "fr echet distance problems in weighted regions"
    - "can we compute the similarity between surfaces"

Connected through hyperedge HE-7 (induced by source paper "four soviets walk the dog with an application to alt s conjecture"):
  with members:
    - "computing the frechet distance with a retractable leash"
    - "how to walk your dog in the mountains with no magic leash"
    - "necklaces convolutions and x y"
    - "on range searching with semialgebraic sets ii"
    - "computing the discrete fr echet distance in subquadratic time"
    - "can we compute the similarity between surfaces"

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)