human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "unsupervised learning for physical interaction through video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "look ahead before you leap end to end active recognition by forecasting the effect of motion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "emergence of exploratory look around behaviors through active observation completion")
  Member "the cityscapes dataset for semantic urban scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "learning to extract motion from videos in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on incorporating semantic prior knowlegde in deep learning through embedding space constraints")
  Member "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "temporal generative adversarial nets with singular value clipping":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "visual dynamics probabilistic future frame synthesis via cross convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformation based adversarial video prediction on large scale data")

Question: Please tell me which class the source paper 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)