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: "speeding up simplification of polygonal curves using nested approximations"
  Abstract: We develop a top-down multiresolution algorithm (TDMR) to solve iteratively the problem of polygonal curve approximation. This algorithm provides nested polygonal approximations of an input curve. We show theoretically and experimentally that, if the simplification algorithm $${\mathcal{A}}$$used between any two successive levels of resolution satisfies some conditions, the multiresolution algorithm will have a complexity lower than the complexity of $${\mathcal{A}}$$applied directly on the input curve to provide the crudest polygonal approximation. In particular, we show that if $${\mathcal{A}}$$has a O(N 2/K) complexity (the complexity of a reduced search dynamic programming solution approach), where N and K are, respectively, the number of segments in the input curve and the number of segments in the crudest approximation, the complexity of MR is in O(N). We experimentally compare the outcomes of TDMR with those of the optimal full search dynamic programming solution and of classical merge and split approaches. The experimental evaluations confirm the theoretical derivations and show that the proposed approach evaluated on 2D coastal maps either leads to a lower complexity or provides polygonal approximations closer to the initial curves.

Connected through hyperedge HE-1 (induced by source paper "adaptive down sampling and dimension reduction in time elastic kernel machines for efficient recognition of isolated gestures"):
  with members:
    - "on recursive edit distance kernels with application to time series classification"
    - "toward a motor theory of sign language perception"
    - "time warp edit distance with stiffness adjustment for time series matching"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-2 (induced by source paper "down sampling coupled to elastic kernel machines for efficient recognition of isolated gestures"):
  with members:
    - "time warp edit distance with stiffness adjustment for time series matching"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-3 (induced by source paper "time warp edit distance with stiffness adjustment for time series matching"):
  with members:
    - "time warp edit distance"

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)