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 with an application to alt s conjecture"
  Abstract: Given two polygonal curves in the plane, there are several ways to define a measure of similarity between them. One measure that has been extremely popular in the past is the Frechet distance. Since it has been proposed by Alt and Godau in 1992, many variants and extensions have been described. However, even 20 years later, the original O(n^2 log n) algorithm by Alt and Godau for computing the Frechet distance remains the state of the art (here n denotes the number of vertices on each curve). This has led Helmut Alt to conjecture that the associated decision problem is 3SUM-hard. #R##N#In recent work, Agarwal et al. show how to break the quadratic barrier for the discrete version of the Frechet distance, where we consider sequences of points instead of polygonal curves. Building on their work, we give an algorithm to compute the Frechet distance between two polygonal curves in time O(n^2 (log n)^(1/2) (\log\log n)^(3/2)) on a pointer machine and in time O(n^2 (loglog n)^2) on a word RAM. Furthermore, we show that there exists an algebraic decision tree for the Frechet problem of depth O(n^(2-epsilon)), for some epsilon > 0. This provides evidence that computing the Frechet distance may not be 3SUM-hard after all and reveals an intriguing new aspect of this well-studied problem.

Connected through hyperedge HE-1 (induced by source paper "fresh fr echet similarity with hashing"):
  with members:
    - "approximate nearest neighbor search in high dimensions"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "locality sensitive hashing of curves"
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast locality sensitive hashing frameworks for approximate near neighbor search"
    - "spatio temporal analysis of team sports"

Connected through hyperedge HE-2 (induced by source paper "probabilistic embeddings of the fr echet distance"):
  with members:
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "dynamic time warping and geometric edit distance breaking the quadratic barrier"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "approximating dynamic time warping and edit distance for a pair of point sequences"
    - "quadratic time hardness of lcs and other sequence similarity measures"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "on the impossibility of dimension reduction for doubling subsets of ell_p p 2"

Connected through hyperedge HE-3 (induced by source paper "fr echet distance under translation conditional hardness and an algorithm via offline dynamic grid reachability"):
  with members:
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"

Connected through hyperedge HE-4 (induced by source paper "a fast implementation of near neighbors queries for frechet distance gis cup"):
  with members:
    - "fr echet distance for curves revisited"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "can we compute the similarity between surfaces"

Connected through hyperedge HE-5 (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"
    - "computing the frechet distance with a retractable leash"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "efficient sampling based bottleneck pathfinding over cost maps"):
  with members:
    - "an efficient algorithm for computing high quality paths amid polygonal obstacles"
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "sampling based bottleneck pathfinding with applications to frechet matching"
    - "time optimal coordination of mobile robots along specified paths"
    - "on the complexity of an unregulated traffic crossing"
    - "motion planning for unlabeled discs with optimality guarantees"
    - "on the hardness of unlabeled multi robot motion planning"
    - "efficient high quality motion planning by fast all pairs r nearest neighbors"

Connected through hyperedge HE-7 (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"
    - "locally correct frechet matchings"
    - "geodesic fr echet distance inside a simple polygon"

Connected through hyperedge HE-8 (induced by source paper "clustering time series under the fr echet distance"):
  with members:
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "how good is the chord algorithm"
    - "on the impossibility of dimension reduction for doubling subsets of ell_p p 2"
    - "approximating k median via pseudo approximation"
    - "jaywalking your dog computing the fr echet distance with shortcuts"
    - "a unified framework for approximating and clustering data"
    - "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)