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: "next flow hybrid multi tasking with next frame prediction to boost optical flow estimation in the wild"
  Abstract: CNN-based optical flow estimators have attracted attentions recently, mainly due to their impressive speed. As successful as they've been on synthetic datasets, they are still far behind the classical methods in real-world scenarios, mainly due to lack of flow ground-truth. In the current work, we seek to boost CNN-based 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 novel time-variant multi-tasking architecture, the network decides which of the tasks to learn at each point of time, depending on the availability of ground-truth. Our proposed method improves optical flow estimation in real scenes dramatically. We also experiment with prediction of "next-flow" instead of estimation of the current flow, which is intuitively more related to the task of next-frame prediction and improve the results even more. We report and evaluate results both qualitatively and quantitatively. The latter is done by training a single-stream action classifier on the estimated flow fields on UCF101 & HMDB51 and demonstrate high improvements of accuracy over the baseline: 10.2% and 9.6% respectively. Also as a side product of this work, we report significant improvements over state-of-the-art results in the task of next-frame prediction.

Connected through hyperedge HE-1 (induced by source paper "better guider predicts future better difference guided generative adversarial networks"):
  with members:
    - "learning to forecast and refine residual motion for image to video generation"
    - "dual motion gan for future flow embedded video prediction"
    - "contextvp fully context aware video prediction"
    - "hp gan probabilistic 3d human motion prediction via gan"
    - "learning to generate long term future via hierarchical prediction"
    - "hierarchical video generation from orthogonal information optical flow and texture"
    - "improved training of wasserstein gans"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"

Connected through hyperedge HE-2 (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"
    - "hybrid learning of optical flow and next frame prediction to boost optical flow in the wild"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "hidden two stream convolutional networks for action recognition"):
  with members:
    - "video enhancement with task oriented flow"
    - "deep texture manifold for ground terrain recognition"
    - "learning optical flow via dilated networks and occlusion reasoning"
    - "towards universal representation for unseen action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "deep local video feature for action recognition"

Connected through hyperedge HE-4 (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"
    - "hybrid learning of optical flow and next frame prediction to boost optical flow in the wild"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "dual motion gan for future flow embedded video prediction"):
  with members:
    - "interpretable structure evolving lstm"
    - "unsupervised learning of long term motion dynamics for videos"
    - "nonparametric variational auto encoders for hierarchical representation learning"
    - "video frame synthesis using deep voxel flow"
    - "recurrent topic transition gan for visual paragraph generation"
    - "optical flow estimation using a spatial pyramid network"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"

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)