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: "video ladder networks"
  Abstract: We present the Video Ladder Network (VLN) for efficiently generating future video frames. VLN is a neural encoder-decoder model augmented at all layers by both recurrent and feedforward lateral connections. At each layer, these connections form a lateral recurrent residual block, where the feedforward connection represents a skip connection and the recurrent connection represents the residual. Thanks to the recurrent connections, the decoder can exploit temporal summaries generated from all layers of the encoder. This way, the top layer is relieved from the pressure of modeling lower-level spatial and temporal details. Furthermore, we extend the basic version of VLN to incorporate ResNet-style residual blocks in the encoder and decoder, which help improving the prediction results. VLN is trained in self-supervised regime on the Moving MNIST dataset, achieving competitive results while having very simple structure and providing fast inference.

Connected through hyperedge HE-1 (induced by source paper "frequency domain transformer networks for video prediction"):
  with members:
    - "location dependency in video prediction"
    - "video pixel networks"

Connected through hyperedge HE-2 (induced by source paper "recurrent flow guided semantic forecasting"):
  with members:
    - "nonlinear 3d face morphable model"
    - "dual motion gan for future flow embedded video prediction"
    - "predicting deeper into the future of semantic segmentation"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "flow guided feature aggregation for video object detection"
    - "unsupervised learning of disentangled representations from video"
    - "pose invariant face alignment with a single cnn"

Connected through hyperedge HE-3 (induced by source paper "predicting the future with transformational states"):
  with members:
    - "predicting deeper into the future of semantic segmentation"
    - "self supervised visual planning with temporal skip connections"
    - "decomposing motion and content for natural video sequence prediction"
    - "unsupervised learning of disentangled representations from video"
    - "deep representation learning for human motion prediction and classification"
    - "visual forecasting by imitating dynamics in natural sequences"
    - "on human motion prediction using recurrent neural networks"
    - "the pose knows video forecasting by generating pose futures"

Connected through hyperedge HE-4 (induced by source paper "sampling free uncertainty estimation in gated recurrent units with exponential families"):
  with members:
    - "lstm a search space odyssey"
    - "bayesian recurrent neural networks"
    - "natural parameter networks a class of probabilistic neural networks"
    - "contextual rnn gans for abstract reasoning diagram generation"
    - "exploring the limits of language modeling"
    - "video pixel networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-5 (induced by source paper "location dependency in video prediction"):
  with members:
    - "recurrent ladder networks"
    - "conditional image generation with pixelcnn decoders"
    - "location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities"
    - "video pixel networks"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "semi supervised learning with ladder networks"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-6 (induced by source paper "folded recurrent neural networks for future video prediction"):
  with members:
    - "dual motion gan for future flow embedded video prediction"
    - "self supervised visual planning with temporal skip connections"
    - "recurrent ladder networks"
    - "decomposing motion and content for natural video sequence prediction"
    - "hybrid learning of optical flow and next frame prediction to boost optical flow in the wild"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "one step time dependent future video frame prediction with a convolutional encoder decoder neural network"

Connected through hyperedge HE-7 (induced by source paper "unsupervised learning of disentangled representations from video"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "recurrent environment simulators"
    - "unsupervised learning for physical interaction through video prediction"
    - "semantic scene completion from a single depth image"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "recurrent ladder networks"):
  with members:
    - "semi supervised phoneme recognition with recurrent ladder networks"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "neural expectation maximization"
    - "on the exact relationship between the denoising function and the data distribution"
    - "unsupervised learning for physical interaction through video prediction"
    - "stacked hourglass networks for human pose estimation"
    - "learning recurrent representations for hierarchical behavior modeling"

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)