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 frechet distance with a retractable leash"
  Abstract: All known algorithms for the Frechet distance between curves proceed in two steps: first, they construct an efficient oracle for the decision version; second, they use this oracle to find the optimum from a finite set of critical values. We present a novel approach that avoids the detour through the decision version. This gives the first quadratic time algorithm for the Frechet distance between polygonal curves in $$\mathbb {R}^d$$Rd under polyhedral distance functions (e.g., $$L_1$$L1 and $$L_\infty $$L?). We also get a $$(1+\varepsilon )$$(1+?)-approximation of the Frechet distance under the Euclidean metric, in quadratic time for any fixed $$\varepsilon > 0$$?>0. For the exact Euclidean case, our framework currently yields an algorithm with running time $$O(n^2 \log ^2 n)$$O(n2log2n). However, we conjecture that it may eventually lead to a faster exact algorithm.

Connected through hyperedge HE-1 (induced by source paper "four soviets walk the dog improved bounds for computing the frechet distance"):
  with members:
    - "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"
    - "locally correct frechet matchings"
    - "on range searching with semialgebraic sets ii"
    - "can we compute the similarity between surfaces"

Connected through hyperedge HE-2 (induced by source paper "sampling based bottleneck pathfinding with applications to frechet matching"):
  with members:
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "motion planning for unlabeled discs with optimality guarantees"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "on the hardness of unlabeled multi robot motion planning"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "efficient high quality motion planning by fast all pairs r nearest neighbors"
    - "efficient multi robot motion planning for unlabeled discs in simple polygons"
    - "simple curve embedding"

Connected through hyperedge HE-3 (induced by source paper "four soviets walk the dog with an application to alt s conjecture"):
  with members:
    - "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"
    - "computing the discrete fr echet distance in subquadratic time"
    - "can we compute the similarity between surfaces"

Connected through hyperedge HE-4 (induced by source paper "locally correct frechet matchings"):
  with members:
    - "four soviets walk the dog improved bounds for computing the frechet distance"
    - "fr echet distance revisited and extended"

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)