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: "hybrid learning of optical flow and next frame prediction to boost optical flow in the wild"
  Abstract: CNN-based optical flow estimation has attracted attention recently, mainly due to its impressively high frame rates. These networks perform well on synthetic datasets, but they are still far behind the classical methods in real-world videos. This is because there is no ground truth optical flow for training these networks on real data. In this paper, we boost CNN-based optical flow estimation in real scenes with the help of the freely available self-supervised task of next-frame prediction. To this end, we train the network in a hybrid way, providing it with a mixture of synthetic and real videos. With the help of a sample-variant multi-tasking architecture, the network is trained on different tasks depending on the availability of ground-truth. We also experiment with the prediction of "next-flow" instead of estimation of the current flow, which is intuitively closer to the task of next-frame prediction and yields favorable results. We demonstrate the improvement in optical flow estimation on the real-world KITTI benchmark. Additionally, we test the optical flow indirectly in an action classification scenario. As a side product of this work, we report significant improvements over state-of-the-art in the task of next-frame prediction.

Connected through hyperedge HE-1 (induced by source paper "temporal interpolation as an unsupervised pretraining task for optical flow estimation"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "accurate optical flow via direct cost volume processing"
    - "colorization as a proxy task for visual understanding"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "unsupervised learning of depth and ego motion from video"
    - "transflow unsupervised motion flow by joint geometric and pixel level estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"

Connected through hyperedge HE-2 (induced by source paper "folded recurrent neural networks for future video prediction"):
  with members:
    - "dual motion gan for future flow embedded video prediction"
    - "self supervised visual planning with temporal skip connections"
    - "recurrent ladder networks"
    - "decomposing motion and content for natural video sequence prediction"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "one step time dependent future video frame prediction with a convolutional encoder decoder neural network"
    - "video frame synthesis using deep voxel flow"

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)