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 end2end voxel2voxel prediction"
  Abstract: Over the last few years deep learning methods have emerged as one of the most prominent approaches for video analysis. However, so far their most successful applications have been in the area of video classification and detection, i.e., problems involving the prediction of a single class label or a handful of output variables per video. Furthermore, while deep networks are commonly recognized as the best models to use in these domains, there is a widespread perception that in order to yield successful results they often require time-consuming architecture search, manual tweaking of parameters and computationally intensive pre-processing or post-processing methods. #R##N#In this paper we challenge these views by presenting a deep 3D convolutional architecture trained end to end to perform voxel-level prediction, i.e., to output a variable at every voxel of the video. Most importantly, we show that the same exact architecture can be used to achieve competitive results on three widely different voxel-prediction tasks: video semantic segmentation, optical flow estimation, and video coloring. The three networks learned on these problems are trained from raw video without any form of preprocessing and their outputs do not require post-processing to achieve outstanding performance. Thus, they offer an efficient alternative to traditional and much more computationally expensive methods in these video domains.

Connected through hyperedge HE-1 (induced by source paper "learning multi human optical flow"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning human optical flow"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "learning from synthetic humans"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-2 (induced by source paper "voxelmorph a learning framework for deformable medical image registration"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "iterative amortized inference"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "inference suboptimality in variational autoencoders"
    - "label driven weakly supervised learning for multimodal deformarle image registration"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "non rigid image registration using fully convolutional networks with deep self supervision"

Connected through hyperedge HE-3 (induced by source paper "face mask extraction in video sequence"):
  with members:
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "end to end semantic face segmentation with conditional random fields as convolutional recurrent and adversarial networks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clockwork convnets for video semantic segmentation"
    - "one shot video object segmentation"
    - "semi supervised domain adaptation for weakly labeled semantic video object segmentation"
    - "visual speech recognition using pca networks and lstms in a tandem gmm hmm system"

Connected through hyperedge HE-4 (induced by source paper "phase collaborative network for two phase medical image segmentation"):
  with members:
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"

Connected through hyperedge HE-5 (induced by source paper "fully hyperbolic convolutional neural networks"):
  with members:
    - "fast video object segmentation with spatio temporal gans"
    - "scale recurrent network for deep image deblurring"
    - "deep neural networks motivated by partial differential equations"
    - "switchable temporal propagation network"
    - "multi level wavelet cnn for image restoration"
    - "the reversible residual network backpropagation without storing activations"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "wavelet convolutional neural networks for texture classification"

Connected through hyperedge HE-6 (induced by source paper "learning conditional deformable templates with convolutional networks"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "unsupervised learning of probabilistic diffeomorphic registration for images and surfaces"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "an unsupervised learning model for deformable medical image registration"
    - "unsupervised probabilistic deformation modeling for robust diffeomorphic registration"
    - "end to end unsupervised deformable image registration with a convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "lgan lung segmentation in ct scans using generative adversarial network"):
  with members:
    - "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "semantic segmentation using adversarial networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning deconvolution network for semantic segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "flexible neural representation for physics prediction"):
  with members:
    - "probing physics knowledge using tools from developmental psychology"
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "3d physnet learning the intuitive physics of non rigid object deformations"
    - "learning to play with intrinsically motivated self aware agents"
    - "visual interaction networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric deep learning going beyond euclidean data"
    - "unsupervised learning for physical interaction through video prediction"

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)