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: "spatio temporal video autoencoder with differentiable memory"
  Abstract: We describe a new spatio-temporal video autoencoder, based on a classic spatial image autoencoder and a novel nested temporal autoencoder. The temporal encoder is represented by a differentiable visual memory composed of convolutional long short-term memory (LSTM) cells that integrate changes over time. Here we target motion changes and use as temporal decoder a robust optical flow prediction module together with an image sampler serving as built-in feedback loop. The architecture is end-to-end differentiable. At each time step, the system receives as input a video frame, predicts the optical flow based on the current observation and the LSTM memory state as a dense transformation map, and applies it to the current frame to generate the next frame. By minimising the reconstruction error between the predicted next frame and the corresponding ground truth next frame, we train the whole system to extract features useful for motion estimation without any supervision effort. We present one direct application of the proposed framework in weakly-supervised semantic segmentation of videos through label propagation using optical flow.

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 "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-3 (induced by source paper "occlusion aware unsupervised learning of optical flow from video"):
  with members:
    - "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"
    - "unsupervised deep epipolar flow for stationary or dynamic scenes"
    - "learning optical flow via dilated networks and occlusion reasoning"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "digging into self supervised monocular depth estimation"

Connected through hyperedge HE-4 (induced by source paper "irregular convolutional auto encoder on point clouds"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "cgans with projection discriminator"
    - "hierarchical graph representation learning with differentiable pooling"
    - "point convolutional neural networks by extension operators"
    - "splatnet sparse lattice networks for point cloud processing"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "local spectral graph convolution for point set feature learning"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-5 (induced by source paper "learning predictive models from observation and interaction"):
  with members:
    - "experience embedded visual foresight"
    - "imitation learning from video by leveraging proprioception"
    - "improved conditional vrnns for video prediction"
    - "learning robotic manipulation through visual planning and acting"
    - "model based reinforcement learning for atari"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "adversarial imitation learning from incomplete demonstrations"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"

Connected through hyperedge HE-6 (induced by source paper "bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "geo supervised visual depth prediction"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "computer vision for autonomous vehicles problems datasets and state of the art"

Connected through hyperedge HE-7 (induced by source paper "scaling autoregressive video models"):
  with members:
    - "model based reinforcement learning for atari"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "generating long sequences with sparse transformers"
    - "image transformer"
    - "a short note about kinetics 600"
    - "blockwise parallel decoding for deep autoregressive models"
    - "stochastic video generation with a learned prior"
    - "towards accurate generative models of video a new metric challenges"

Connected through hyperedge HE-8 (induced by source paper "learning to predict without looking ahead world models without forward prediction"):
  with members:
    - "weight agnostic neural networks"
    - "reinforcement learning for improving agent design"
    - "model based reinforcement learning for atari"
    - "videoflow a flow based generative model for video"
    - "deep neuroevolution of recurrent and discrete world models"
    - "learning latent dynamics for planning from pixels"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "lipschitz continuity in model based reinforcement learning"

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)