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: "image sequence interpolation using optimal control"
  Abstract: The problem of the generation of an intermediate image between two given images in an image sequence is considered. The problem is formulated as an optimal control problem governed by a transport equation. This approach bears similarities with the Horn \& Schunck method for optical flow calculation but in fact the model is quite different. The images are modelled in $BV$ and an analysis of solutions of transport equations with values in $BV$ is included. Moreover, the existence of optimal controls is proven and necessary conditions are derived. Finally, two algorithms are given and numerical results are compared with existing methods. The new method is competitive with state-of-the-art methods and even outperforms several existing methods.

Connected through hyperedge HE-1 (induced by source paper "a temporally aware interpolation network for video frame inpainting"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "decomposing motion and content for natural video sequence prediction"
    - "improving consistency and correctness of sequence inpainting using semantically guided generative adversarial network"
    - "deep stacked networks with residual polishing for image inpainting"
    - "video frame interpolation via adaptive convolution"
    - "video frame interpolation via adaptive separable convolution"
    - "video frame synthesis using deep voxel flow"
    - "high resolution image inpainting using multi scale neural patch synthesis"

Connected through hyperedge HE-2 (induced by source paper "multi view frame reconstruction with conditional gan"):
  with members:
    - "a temporally aware interpolation network for video frame inpainting"
    - "decomposing motion and content for natural video sequence prediction"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "long term video interpolation with bidirectional predictive network"
    - "video frame interpolation via adaptive separable convolution"
    - "frame interpolation with multi scale deep loss functions and generative adversarial networks"
    - "video frame synthesis using deep voxel flow"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-3 (induced by source paper "distributed memory large deformation diffeomorphic 3d image registration"):
  with members:
    - "accfft a library for distributed memory fft on cpu and gpu architectures"

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)