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: "simple curve embedding"
  Abstract: Given a curve f and a surface S, how hard is it to find a simple curve f' in S that is the most similar to f? #R##N#We introduce and study this simple curve embedding problem for piecewise linear curves and surfaces in R^2 and R^3, under Hausdorff distance, weak Frechet distance, and Frechet distance as similarity measures for curves. Surprisingly, while several variants of the problem turn out to have polynomial-time solutions, we show that in R^3 the simple curve embedding problem is NP-hard under Frechet distance even if S is a plane, as well as under weak Frechet distance if S is a terrain. Additionally, these results give insight into the difficulty of computing the Frechet distance between surfaces, and they imply that the partial Frechet distance between non-planar surfaces is NP-hard as well.

Connected through hyperedge HE-1 (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-2 (induced by source paper "discretized approaches to schematization"):
  with members:
    - "solving the maximum weight connected subgraph problem to optimality"
    - "intermittent map matching with the discrete frechet distance"
    - "map matching with simplicity constraints"
    - "locally correct frechet matchings"

Connected through hyperedge HE-3 (induced by source paper "how to walk your dog in the mountains with no magic leash"):
  with members:
    - "measuring similarity between curves on 2 manifolds via homotopy area"
    - "computing the fr e chet distance with shortcuts is np hard"

Connected through hyperedge HE-4 (induced by source paper "map matching with simplicity constraints"):
  with members:
    - "four soviets walk the dog with an application to alt s conjecture"
    - "the problem of compatible representatives"

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)