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: "learning to extract motion from videos in convolutional neural networks"
  Abstract: This paper shows how to extract dense optical flow from videos with a convolutional neural network (CNN). The proposed model constitutes a potential building block for deeper architectures to allow using motion without resorting to an external algorithm, \eg for recognition in videos. We derive our network architecture from signal processing principles to provide desired invariances to image contrast, phase and texture. We constrain weights within the network to enforce strict rotation invariance and substantially reduce the number of parameters to learn. We demonstrate end-to-end training on only 8 sequences of the Middlebury dataset, orders of magnitude less than competing CNN-based motion estimation methods, and obtain comparable performance to classical methods on the Middlebury benchmark. Importantly, our method outputs a distributed representation of motion that allows representing multiple, transparent motions, and dynamic textures. Our contributions on network design and rotation invariance offer insights nonspecific to motion estimation.

Connected through hyperedge HE-1 (induced by source paper "on incorporating semantic prior knowlegde in deep learning through embedding space constraints"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "actively seeking and learning from live data"
    - "hyperbolic neural networks"
    - "compositional attention networks for machine reasoning"
    - "deep learning a critical appraisal"
    - "visual question answering as a meta learning task"
    - "don t just assume look and answer overcoming priors for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "net2vis a visual grammar for automatically generating publication ready cnn architecture visualizations"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "single image tomography 3d volumes from 2d cranial x rays"
    - "cnncomparator comparative analytics of convolutional neural networks"
    - "ssgan secure steganography based on generative adversarial networks"
    - "deep shading convolutional neural networks for screen space shading"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"

Connected through hyperedge HE-3 (induced by source paper "learning non parametric invariances from data with permanent random connectomes"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "autoaugment learning augmentation policies from data"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deep rotation equivariant network"
    - "graph based isometry invariant representation learning"
    - "aggregated residual transformations for deep neural networks"
    - "warped convolutions efficient invariance to spatial transformations"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-4 (induced by source paper "end to end learning of motion representation for video understanding"):
  with members:
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"
    - "hidden two stream convolutional networks for action recognition"
    - "attention clusters purely attention based local feature integration for video classification"
    - "fast optical flow using dense inverse search"
    - "actionflownet learning motion representation for action recognition"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-5 (induced by source paper "devon deformable volume network for learning optical flow"):
  with members:
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "learning image relations with contrast association networks"
    - "optical flow in mostly rigid scenes"
    - "accurate optical flow via direct cost volume processing"
    - "deformable convolutional networks"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "demon depth and motion network for learning monocular stereo"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "non parametric transformation networks"):
  with members:
    - "max margin invariant features from transformed unlabeled data"
    - "dynamic routing between capsules"
    - "deep rotation equivariant network"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "graph based isometry invariant representation learning"
    - "harmonic networks deep translation and rotation equivariance"
    - "densely connected convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "phasenet for video frame interpolation"):
  with members:
    - "learning to predict indoor illumination from a single image"
    - "video frame interpolation via adaptive convolution"
    - "the 2017 davis challenge on video object segmentation"
    - "video frame interpolation via adaptive separable convolution"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "view synthesis by appearance flow"
    - "generating videos with scene dynamics"

Connected through hyperedge HE-8 (induced by source paper "pixelnet representation of the pixels by the pixels and for the pixels"):
  with members:
    - "an implementation of faster rcnn with study for region sampling"
    - "convolutional pose machines"
    - "learning representations for automatic colorization"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "ambient sound provides supervision for visual learning"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"

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)