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: "deep predictive coding networks for video prediction and unsupervised learning"
  Abstract: While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning - leveraging unlabeled examples to learn about the structure of a domain - remains a difficult unsolved challenge. Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world. We describe a predictive neural network ("PredNet") architecture that is inspired by the concept of "predictive coding" from the neuroscience literature. These networks learn to predict future frames in a video sequence, with each layer in the network making local predictions and only forwarding deviations from those predictions to subsequent network layers. We show that these networks are able to robustly learn to predict the movement of synthetic (rendered) objects, and that in doing so, the networks learn internal representations that are useful for decoding latent object parameters (e.g. pose) that support object recognition with fewer training views. We also show that these networks can scale to complex natural image streams (car-mounted camera videos), capturing key aspects of both egocentric movement and the movement of objects in the visual scene, and the representation learned in this setting is useful for estimating the steering angle. Altogether, these results suggest that prediction represents a powerful framework for unsupervised learning, allowing for implicit learning of object and scene structure.

Connected through hyperedge HE-1 (induced by source paper "videoflow a conditional flow based model for stochastic video generation"):
  with members:
    - "waveglow a flow based generative network for speech synthesis"
    - "stochastic adversarial video prediction"
    - "flow grounded spatial temporal video prediction from still images"
    - "stochastic video generation with a learned prior"
    - "towards accurate generative models of video a new metric challenges"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "fast generation for convolutional autoregressive models"
    - "self supervised visual planning with temporal skip connections"

Connected through hyperedge HE-2 (induced by source paper "convolutional tensor train lstm for spatio temporal learning"):
  with members:
    - "a tensorized transformer for language modeling"
    - "efficient n dimensional convolutions via higher order factorization"
    - "robust deep networks with randomized tensor regression layers"
    - "scaling autoregressive video models"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-3 (induced by source paper "univilm a unified video and language pre training model for multimodal understanding and generation"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "learning video representations using contrastive bidirectional transformer"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "mass masked sequence to sequence pre training for language generation"
    - "a case study on combining asr and visual features for generating instructional video captions"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "multimodal abstractive summarization for how2 videos"
    - "videobert a joint model for video and language representation learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "photo realistic video prediction on natural videos of largely changing frames"):
  with members:
    - "disentangling propagation and generation for video prediction"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "dual motion gan for future flow embedded video prediction"
    - "contextvp fully context aware video prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "video frame synthesis using deep voxel flow"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-6 (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-7 (induced by source paper "learning for video compression"):
  with members:
    - "learning based facial image compression with semantic fidelity metric"
    - "convolutional neural network based block up sampling for intra frame coding"
    - "an end to end compression framework based on convolutional neural networks"
    - "a convolutional neural network approach for half pel interpolation in video coding"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "neural network based arithmetic coding of intra prediction modes in hevc"
    - "learning to inpaint for image compression"

Connected through hyperedge HE-8 (induced by source paper "filter grafting for deep neural networks"):
  with members:
    - "omni scale feature learning for person re identification"
    - "importance estimation for neural network pruning"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "network transplanting"
    - "scsp spectral clustering filter pruning with soft self adaption manners"
    - "network compression using correlation analysis of layer responses"

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)