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: "computing the discrete fr echet distance in subquadratic time"
  Abstract: The Fr\'echet distance is a similarity measure between two curves $A$ and $B$: Informally, it is the minimum length of a leash required to connect a dog, constrained to be on $A$, and its owner, constrained to be on $B$, as they walk without backtracking along their respective curves from one endpoint to the other. The advantage of this measure on other measures such as the Hausdorff distance is that it takes into account the ordering of the points along the curves. #R##N#The discrete Fr\'echet distance replaces the dog and its owner by a pair of frogs that can only reside on $n$ and $m$ specific pebbles on the curves $A$ and $B$, respectively. These frogs hop from a pebble to the next without backtracking. The discrete Fr\'echet distance can be computed by a rather straightforward quadratic dynamic programming algorithm. However, despite a considerable amount of work on this problem and its variations, there is no subquadratic algorithm known, even for approximation versions of the problem. #R##N#In this paper we present a subquadratic algorithm for computing the discrete Fr\'echet distance between two sequences of points in the plane, of respective lengths $m\le n$. The algorithm runs in $O(\dfrac{mn\log\log n}{\log n})$ time and uses $O(n+m)$ storage. Our approach uses the geometry of the problem in a subtle way to encode legal positions of the frogs as states of a finite automata.

Connected through hyperedge HE-1 (induced by source paper "efficient nearest neighbor query and clustering of planar curves"):
  with members:
    - "locality sensitive hashing of curves"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a faster algorithm for the discrete frechet distance under translation"):
  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"

Connected through hyperedge HE-3 (induced by source paper "finding a curve in a point set"):
  with members:
    - "hardness results on curve point set matching with frechet distance"

Connected through hyperedge HE-4 (induced by source paper "the discrete and semi continuous fr echet distance with shortcuts via approximate distance counting and selection techniques"):
  with members:
    - "net and prune a linear time algorithm for euclidean distance problems"
    - "computing the fr e chet distance with shortcuts is np hard"
    - "four soviets walk the dog with an application to alt s conjecture"

Connected through hyperedge HE-5 (induced by source paper "measuring similarity between curves on 2 manifolds via homotopy area"):
  with members:
    - "how to walk your dog in the mountains with no magic leash"
    - "four soviets walk the dog with an application to alt s conjecture"

Connected through hyperedge HE-6 (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"
    - "locally correct frechet matchings"
    - "necklaces convolutions and x y"
    - "on range searching with semialgebraic sets ii"
    - "can we compute the similarity between surfaces"

Connected through hyperedge HE-7 (induced by source paper "jaywalking your dog computing the fr echet distance with shortcuts"):
  with members:
    - "four soviets walk the dog with an application to alt s conjecture"
    - "approximating the fr echet distance for realistic curves in near linear time"

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)