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 "long term video interpolation with bidirectional predictive network"):
  Abstract: This paper considers the challenging task of long-term video interpolation. Unlike most existing methods that only generate few intermediate frames between existing adjacent ones, we attempt to speculate or imagine the procedure of an episode and further generate multiple frames between two non-consecutive frames in videos. In this paper, we present a novel deep architecture called bidirectional predictive network (BiPN) that predicts intermediate frames from two opposite directions. The bidirectional architecture allows the model to learn scene transformation with time as well as generate longer video sequences. Besides, our model can be extended to predict multiple possible procedures by sampling different noise vectors. A joint loss composed of clues in image and feature spaces and adversarial loss is designed to train our model. We demonstrate the advantages of BiPN on two benchmarks Moving 2D Shapes and UCF101 and report competitive results to recent approaches.

Includes members:
  Member "learning to generate long term future via hierarchical prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "video frame interpolation via adaptive convolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "softmax splatting for video frame interpolation")
  Member "video frame synthesis using deep voxel flow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "generating videos with scene dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "deep multi scale video prediction beyond mean square error":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "epicflow edge preserving interpolation of correspondences for optical flow":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")
  Member "learning to generate chairs with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints")
  Member "ucf101 a dataset of 101 human actions classes from videos in the wild":
    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)