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: "active spline model a shape based model interactive segmentation"
  Abstract: Rarely in literature a method of segmentation cares for the edit after the algorithm delivers. They provide no solution when segmentation goes wrong. We propose to formulate point distribution model in terms of centripetal-parameterized Catmull-Rom spline. Such fusion brings interactivity to model-based segmentation, so that edit is better handled. When the delivered segment is unsatisfactory, user simply shifts points to vary the curve. We ran the method on three disparate imaging modalities and achieved an average overlap of 0.879 for automated lung segmentation on chest radiographs. The edit afterward improved the average overlap to 0.945, with a minimum of 0.925. The source code and the demo video are available at this http URL

Connected through hyperedge HE-1 (induced by source paper "fast interactive object annotation with curve gcn"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "path aggregation network for instance segmentation"
    - "geometric deep learning going beyond euclidean data"
    - "annotating object instances with a polygon rnn"
    - "neural 3d mesh renderer"

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.CV(Computer Vision and Pattern Recognition)