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 visual structure using predictive generative networks"
  Abstract: The ability to predict future states of the environment is a central pillar of intelligence. At its core, effective prediction requires an internal model of the world and an understanding of the rules by which the world changes. Here, we explore the internal models developed by deep neural networks trained using a loss based on predicting future frames in synthetic video sequences, using a CNN-LSTM-deCNN framework. We first show that this architecture can achieve excellent performance in visual sequence prediction tasks, including state-of-the-art performance in a standard 'bouncing balls' dataset (Sutskever et al., 2009). Using a weighted mean-squared error and adversarial loss (Goodfellow et al., 2014), the same architecture successfully extrapolates out-of-the-plane rotations of computer-generated faces. Furthermore, despite being trained end-to-end to predict only pixel-level information, our Predictive Generative Networks learn a representation of the latent structure of the underlying three-dimensional objects themselves. Importantly, we find that this representation is naturally tolerant to object transformations, and generalizes well to new tasks, such as classification of static images. Similar models trained solely with a reconstruction loss fail to generalize as effectively. We argue that prediction can serve as a powerful unsupervised loss for learning rich internal representations of high-level object features.

Connected through hyperedge HE-1 (induced by source paper "joint visual temporal embedding for unsupervised learning of actions in untrimmed sequences"):
  with members:
    - "a hybrid rnn hmm approach for weakly supervised temporal action segmentation"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "weakly supervised energy based learning for action segmentation"
    - "unsupervised learning of action classes with continuous temporal embedding"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "unsupervised learning and segmentation of complex activities from video"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "a perceptual prediction framework for self supervised event segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep variational luenberger type observer for stochastic video prediction"):
  with members:
    - "video prediction for precipitation nowcasting"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "disentangling factors of variation using few labels"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "unsupervised discovery of parts structure and dynamics"
    - "recurrent kalman networks factorized inference in high dimensional deep feature spaces"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"

Connected through hyperedge HE-3 (induced by source paper "point to point video generation"):
  with members:
    - "diversity sensitive conditional generative adversarial networks"
    - "phasenet for video frame interpolation"
    - "conditional video generation using action appearance captions"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "self supervised learning of a facial attribute embedding from video"
    - "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"

Connected through hyperedge HE-4 (induced by source paper "gated gan adversarial gated networks for multi collection style transfer"):
  with members:
    - "activation maximization generative adversarial nets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "style transfer via texture synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "towards corner case detection for autonomous driving"):
  with members:
    - "pixel deconvolutional networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "lidar and camera detection fusion in a real time industrial multi sensor collision avoidance system"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "future frame prediction for anomaly detection a new baseline"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "graph embedded pose clustering for anomaly detection"):
  with members:
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
    - "pose flow efficient online pose tracking"
    - "deep clustering for unsupervised learning of visual features"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "latent space autoregression for novelty detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "spacenet mvoi a multi view overhead imagery dataset"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "xview objects in context in overhead imagery"
    - "spatially transformed adversarial examples"
    - "an occluded stacked hourglass approach to facial landmark localization and occlusion estimation"
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "you only look twice rapid multi scale object detection in satellite imagery"

Connected through hyperedge HE-8 (induced by source paper "inception inspired lstm for next frame video prediction"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "lstm a search space odyssey"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep visual foresight for planning robot motion"
    - "anomaly detection in video using predictive convolutional long short term memory networks"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "wide residual networks"
    - "end to end learning of driving models from large scale video datasets"

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.LG(Machine Learning)