human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "four soviets walk the dog improved bounds for computing the frechet distance"):
  Abstract: Given two polygonal curves in the plane, there are many ways to define a notion of similarity between them. One popular measure is the Frechet distance. Since it was proposed by Alt and Godau in 1992, many variants and extensions have been studied. Nonetheless, even more than 20 years later, the original $$O(n^2 \log n)$$O(n2logn) algorithm by Alt and Godau for computing the Frechet distance remains the state of the art (here, n denotes the number of edges on each curve). This has led Helmut Alt to conjecture that the associated decision problem is 3SUM-hard. In recent work, Agarwal et al. show how to break the quadratic barrier for the discrete version of the Frechet distance, where one considers sequences of points instead of polygonal curves. Building on their work, we give a randomized algorithm to compute the Frechet distance between two polygonal curves in time $$O(n^2 \sqrt{\log n}(\log \log n)^{3/2})$$O(n2logn(loglogn)3/2) on a pointer machine and in time $$O(n^2(\log \log n)^2)$$O(n2(loglogn)2) on a word RAM. Furthermore, we show that there exists an algebraic decision tree for the decision problem of depth $$O(n^{2-\varepsilon })$$O(n2-ź), for some $$\varepsilon > 0$$ź>0. We believe that this reveals an intriguing new aspect of this well-studied problem. Finally, we show how to obtain the first subquadratic algorithm for computing the weak Frechet distance on a word RAM.

Includes members:
  Member "computing the frechet distance with a retractable leash":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sampling based bottleneck pathfinding with applications to frechet matching")
  Member "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graphs cannot be indexed in polynomial time for sub quadratic time string matching unless seth fails")
  Member "threesomes degenerates and love triangles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the fine grained complexity of parity problems")
  Member "how to walk your dog in the mountains with no magic leash":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "homotopy height grid major height and graph drawing height")
  Member "locally correct frechet matchings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discretized approaches to schematization")
  Member "on range searching with semialgebraic sets ii":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithmic and geometric aspects of data depth with focus on β skeleton depth")
  Member "can we compute the similarity between surfaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "computing the frechet distance of trees and graphs of bounded tree width")

Question: Please tell me which class the source paper 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)