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: "im2flow motion hallucination from static images for action recognition"
  Abstract: Existing methods to recognize actions in static images take the images at their face value, learning the appearances---objects, scenes, and body poses---that distinguish each action class. However, such models are deprived of the rich dynamic structure and motions that also define human activity. We propose an approach that hallucinates the unobserved future motion implied by a single snapshot to help static-image action recognition. The key idea is to learn a prior over short-term dynamics from thousands of unlabeled videos, infer the anticipated optical flow on novel static images, and then train discriminative models that exploit both streams of information. Our main contributions are twofold. First, we devise an encoder-decoder convolutional neural network and a novel optical flow encoding that can translate a static image into an accurate flow map. Second, we show the power of hallucinated flow for recognition, successfully transferring the learned motion into a standard two-stream network for activity recognition. On seven datasets, we demonstrate the power of the approach. It not only achieves state-of-the-art accuracy for dense optical flow prediction, but also consistently enhances recognition of actions and dynamic scenes.

Connected through hyperedge HE-1 (induced by source paper "ego pose estimation and forecasting as real time pd control"):
  with members:
    - "xr egopose egocentric 3d human pose from an hmd camera"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning 3d human dynamics from video"
    - "sfv reinforcement learning of physical skills from videos"
    - "flow grounded spatial temporal video prediction from still images"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "unsupervised learning of disentangled representations from video"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-2 (induced by source paper "dmc net generating discriminative motion cues for fast compressed video action recognition"):
  with members:
    - "end to end learning of motion representation for video understanding"
    - "multi fiber networks for video recognition"
    - "contextual multi scale region convolutional 3d network for activity detection"
    - "stochastic adversarial video prediction"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "predicting 3d human dynamics from video"):
  with members:
    - "xnect real time multi person 3d human pose estimation with a single rgb camera"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "deep lip reading a comparison of models and an online application"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "learning 3d human dynamics from video"

Connected through hyperedge HE-4 (induced by source paper "multimodal image outpainting with regularized normalized diversification"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "object driven text to image synthesis via adversarial training"
    - "normalized diversification"
    - "putting humans in a scene learning affordance in 3d indoor environments"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "binge watching scaling affordance learning from sitcoms"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "esrgan enhanced super resolution generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "animating landscape self supervised learning of decoupled motion and appearance for single image video synthesis"):
  with members:
    - "manipulating attributes of natural scenes via hallucination"
    - "flow grounded spatial temporal video prediction from still images"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "personalized cinemagraphs using semantic understanding and collaborative learning"
    - "contextvp fully context aware video prediction"
    - "progressive growing of gans for improved quality stability and variation"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-6 (induced by source paper "hallucinet ing spatiotemporal representations using 2d cnn"):
  with members:
    - "design light weight 3d convolutional networks for video recognition temporal residual fully separable block and fast algorithm"
    - "what and how well you performed a multitask learning approach to action quality assessment"
    - "efficient video classification using fewer frames"
    - "temporal sequence distillation towards few frame action recognition in videos"
    - "motion feature network fixed motion filter for action recognition"
    - "rgb d based action recognition with light weight 3d convolutional networks"
    - "the pros and cons rank aware temporal attention for skill determination in long videos"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-7 (induced by source paper "hallucinating optical flow features for video classification"):
  with members:
    - "spatio temporal video re localization by warp lstm"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "non local netvlad encoding for video classification"
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "video re localization"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "reconstruction network for video captioning"

Connected through hyperedge HE-8 (induced by source paper "exploiting motion information from unlabeled videos for static image action recognition"):
  with members:
    - "unsupervised representation learning by predicting image rotations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "unsupervised representation learning by sorting sequences"
    - "pose from action unsupervised learning of pose features based on motion"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "split brain autoencoders unsupervised learning by cross channel prediction"
    - "self supervised video representation learning with odd one out 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)