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: "unsupervised learning of long term motion dynamics for videos"
  Abstract: We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, Depth, and RGB-D videos.

Connected through hyperedge HE-1 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction"):
  with members:
    - "differentiable physics informed graph networks"
    - "improved conditional vrnns for video prediction"
    - "neural jump stochastic differential equations"
    - "unsupervised learning of object structure and dynamics from videos"
    - "compositional video prediction"
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "recurrent kalman networks factorized inference in high dimensional deep feature spaces"
    - "memory in memory a predictive neural network for learning higher order non stationarity from spatiotemporal dynamics"

Connected through hyperedge HE-2 (induced by source paper "contrastive multiview coding"):
  with members:
    - "learning robust and multilingual speech representations"
    - "self supervised learning of video induced visual invariances"
    - "end to end learning of visual representations from uncurated instructional videos"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "randaugment practical data augmentation with no separate search"
    - "a survey of multi view representation learning"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-3 (induced by source paper "learning correspondence from the cycle consistency of time"):
  with members:
    - "recycle gan unsupervised video retargeting"
    - "deep clustering for unsupervised learning of visual features"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "efficient video object segmentation via network modulation"
    - "adaptive temporal encoding network for video instance level human parsing"
    - "switchable temporal propagation network"
    - "tracking emerges by colorizing videos"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-4 (induced by source paper "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "video representation learning using discriminative pooling"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "sphereface deep hypersphere embedding for face recognition"
    - "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "automatic video object segmentation via motion appearance stream fusion and instance aware segmentation"):
  with members:
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "maskrnn instance level video object segmentation"
    - "video object segmentation with re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-6 (induced by source paper "learning with privileged information via adversarial discriminative modality distillation"):
  with members:
    - "fast converging conditional generative adversarial networks for image synthesis"
    - "modality distillation with multiple stream networks for action recognition"
    - "adversarial network compression"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "interpretable 3d human action analysis with temporal convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "video generation from single semantic label map"):
  with members:
    - "video to video synthesis"
    - "synthesizing images of humans in unseen poses"
    - "flow grounded spatial temporal video prediction from still images"
    - "stochastic video generation with a learned prior"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "image generation from scene graphs"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-8 (induced by source paper "context aware human motion prediction"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "bihmp gan bidirectional 3d human motion prediction gan"
    - "an lstm network for highway trajectory prediction"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic graph cnn for learning on point clouds"
    - "videos as space time region graphs"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "structure inference net object detection using scene level context and instance level relationships"

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)