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: "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.

Connected through hyperedge HE-1 (induced by source paper "rock climber distance frogs versus dogs"):
  with members:
    - "the k fr echet distance"
    - "recognizing weakly simple polygons"
    - "an optimal algorithm for minimum link rectilinear paths in triangulated rectilinear domains"
    - "detecting weakly simple polygons"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "minimum link paths revisited"
    - "the problem of compatible representatives"

Connected through hyperedge HE-2 (induced by source paper "walking the dog fast in practice algorithm engineering of the fr echet distance"):
  with members:
    - "approximating k ell center clustering for curves"
    - "fresh fr echet similarity with hashing"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "intermittent map matching with the discrete frechet distance"

Connected through hyperedge HE-3 (induced by source paper "locally correct frechet matchings"):
  with members:
    - "computing the frechet distance with a retractable leash"
    - "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)