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: "geometry based next frame prediction from monocular video"
  Abstract: We consider the problem of next frame prediction from video input. A recurrent convolutional neural network is trained to predict depth from monocular video input, which, along with the current video image and the camera trajectory, can then be used to compute the next frame. Unlike prior next-frame prediction approaches, we take advantage of the scene geometry and use the predicted depth for generating the next frame prediction. Our approach can produce rich next frame predictions which include depth information attached to each pixel. Another novel aspect of our approach is that it predicts depth from a sequence of images (e.g. in a video), rather than from a single still image. We evaluate the proposed approach on the KITTI dataset, a standard dataset for benchmarking tasks relevant to autonomous driving. The proposed method produces results which are visually and numerically superior to existing methods that directly predict the next frame. We show that the accuracy of depth prediction improves as more prior frames are considered.

Connected through hyperedge HE-1 (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-2 (induced by source paper "imitation learning for human pose prediction"):
  with members:
    - "procedure planning in instructional videos"
    - "peeking into the future predicting future person activities and locations in videos"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "action agnostic human pose forecasting"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning plannable representations with causal infogan"
    - "predicting deeper into the future of semantic segmentation"
    - "hp gan probabilistic 3d human motion prediction via gan"

Connected through hyperedge HE-3 (induced by source paper "unsupervised monocular depth and ego motion learning with structure and semantics"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "unsupervised learning of geometry with edge aware depth normal consistency"

Connected through hyperedge HE-4 (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"
    - "folded recurrent neural networks for future video prediction"
    - "progressive growing of gans for improved quality stability and variation"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-5 (induced by source paper "action agnostic human pose forecasting"):
  with members:
    - "anticipation in human robot cooperation a recurrent neural network approach for multiple action sequences prediction"
    - "predicting deeper into the future of semantic segmentation"
    - "hp gan probabilistic 3d human motion prediction via gan"
    - "learning human motion models for long term predictions"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "deep representation learning for human motion prediction and classification"
    - "forecasting human dynamics from static images"
    - "human pose forecasting via deep markov models"

Connected through hyperedge HE-6 (induced by source paper "sdcnet video prediction using spatially displaced convolution"):
  with members:
    - "stochastic video generation with a learned prior"
    - "predicting deeper into the future of semantic segmentation"
    - "decomposing motion and content for natural video sequence prediction"
    - "contextvp fully context aware video prediction"
    - "folded recurrent neural networks for future video prediction"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "stochastic variational video prediction"
    - "video frame interpolation via adaptive convolution"

Connected through hyperedge HE-7 (induced by source paper "novel view synthesis for large scale scene using adversarial loss"):
  with members:
    - "unsupervised learning for physical interaction through video prediction"
    - "image to image translation with conditional adversarial networks"
    - "multi view 3d models from single images with a convolutional network"
    - "spatial transformer networks"
    - "adam a method for stochastic optimization"
    - "conditional generative adversarial nets"
    - "learning to generate chairs with convolutional neural networks"
    - "depth map prediction from a single image using a multi scale deep network"

Connected through hyperedge HE-8 (induced by source paper "human pose forecasting via deep markov models"):
  with members:
    - "on human motion prediction using recurrent neural networks"
    - "unsupervised learning for physical interaction through video prediction"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "multi person pose estimation with local joint to person associations"

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)