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: "folded recurrent neural networks for future video prediction"
  Abstract: Future video prediction is an ill-posed Computer Vision problem that recently received much attention. Its main challenges are the high variability in video content, the propagation of errors through time, and the non-specificity of the future frames: given a sequence of past frames there is a continuous distribution of possible futures. This work introduces bijective Gated Recurrent Units, a double mapping between the input and output of a GRU layer. This allows for recurrent auto-encoders with state sharing between encoder and decoder, stratifying the sequence representation and helping to prevent capacity problems. We show how with this topology only the encoder or decoder needs to be applied for input encoding and prediction, respectively. This reduces the computational cost and avoids re-encoding the predictions when generating a sequence of frames, mitigating the propagation of errors. Furthermore, it is possible to remove layers from an already trained model, giving an insight to the role performed by each layer and making the model more explainable. We evaluate our approach on three video datasets, outperforming state of the art prediction results on MMNIST and UCF101, and obtaining competitive results on KTH with 2 and 3 times less memory usage and computational cost than the best scored approach.

Connected through hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data"):
  with members:
    - "a short note on the kinetics 700 human action dataset"
    - "large scale adversarial representation learning"
    - "high fidelity speech synthesis with adversarial networks"
    - "adversarial video generation on complex datasets"
    - "scaling autoregressive video models"
    - "object oriented dynamics predictor"
    - "tganv2 efficient training of large models for video generation with multiple subsampling layers"
    - "sequential attend infer repeat generative modelling of moving objects"

Connected through hyperedge HE-2 (induced by source paper "sequential forecasting of 100 000 points"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "conditional invertible flow for point cloud generation"
    - "grip graph based interaction aware trajectory prediction"
    - "probabilistic reconstruction networks for 3d shape inference from a single image"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "human motion trajectory prediction a survey"
    - "pixor real time 3d object detection from point clouds"
    - "a baseline for 3d multi object tracking"

Connected through hyperedge HE-3 (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-4 (induced by source paper "order matters shuffling sequence generation for video prediction"):
  with members:
    - "towards universal representation for unseen action recognition"
    - "stochastic adversarial video prediction"
    - "robust cross view gait identification with evidence a discriminant gait gan diggan approach on 10000 people"
    - "learning to decompose and disentangle representations for video prediction"
    - "visual robot task planning"
    - "decomposing motion and content for natural video sequence prediction"
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-5 (induced by source paper "learning to decompose and disentangle representations for video prediction"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "visual robot task planning"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "unsupervised learning of disentangled representations from video"
    - "neural expectation maximization"
    - "mocogan decomposing motion and content for video generation"

Connected through hyperedge HE-6 (induced by source paper "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"):
  with members:
    - "stochastic video generation with a learned prior"
    - "dual motion gan for future flow embedded video prediction"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "progressive growing of gans for improved quality stability and variation"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-7 (induced by source paper "msnet mutual suppression network for disentangled video representations"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "unsupervised learning of disentangled representations from video"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "an end to end 3d convolutional neural network for action detection and segmentation in videos"
    - "mocogan decomposing motion and content for video generation"

Connected through hyperedge HE-8 (induced by source paper "memory in memory a predictive neural network for learning higher order non stationarity from spatiotemporal dynamics"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "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"
    - "decomposing motion and content for natural video sequence prediction"
    - "unsupervised learning of disentangled representations from video"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "mocogan decomposing motion and content for video generation"

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)