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: "patchbatch a batch augmented loss for optical flow"
  Abstract: We propose a new pipeline for optical flow computation, based on Deep Learning techniques. We suggest using a Siamese CNN to independently, and in parallel, compute the descriptors of both images. The learned descriptors are then compared efficiently using the L2 norm and do not require network processing of patch pairs. The success of the method is based on an innovative loss function that computes higher moments of the loss distributions for each training batch. Combined with an Approximate Nearest Neighbor patch matching method and a flow interpolation technique, state of the art performance is obtained on the most challenging and competitive optical flow benchmarks.

Connected through hyperedge HE-1 (induced by source paper "scopeflow dynamic scene scoping for optical flow"):
  with members:
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "iterative residual refinement for joint optical flow and occlusion estimation"
    - "ddflow learning optical flow with unlabeled data distillation"
    - "selflow self supervised learning of optical flow"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "a fusion approach for multi frame optical flow estimation"
    - "dgc net dense geometric correspondence network"

Connected through hyperedge HE-2 (induced by source paper "iterative residual refinement for joint optical flow and occlusion estimation"):
  with members:
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "a fusion approach for multi frame optical flow estimation"
    - "proflow learning to predict optical flow"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "segmentation aware convolutional networks using local attention masks"
    - "optical flow in mostly rigid scenes"

Connected through hyperedge HE-3 (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-4 (induced by source paper "gcnv2 efficient correspondence prediction for real time slam"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "single view stereo matching"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "superpoint self supervised interest point detection and description"
    - "convolutional neural network architecture for geometric matching"
    - "improving landmark localization with semi supervised learning"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-5 (induced by source paper "bridging stereo matching and optical flow via spatiotemporal correspondence"):
  with members:
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "joint unsupervised learning of optical flow and depth by watching stereo videos"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "single view stereo matching"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "pyramid stereo matching network"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-6 (induced by source paper "sdc stacked dilated convolution a unified descriptor network for dense matching tasks"):
  with members:
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "understanding convolution for semantic segmentation"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"

Connected through hyperedge HE-7 (induced by source paper "hierarchical metric learning and matching for 2d and 3d geometric correspondences"):
  with members:
    - "deep supervision with intermediate concepts"
    - "the earth ain t flat monocular reconstruction of vehicles on steep and graded roads from a moving camera"
    - "learning spread out local feature descriptors"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "sfm net learning of structure and motion from video"
    - "semantic texture for robust dense tracking"

Connected through hyperedge HE-8 (induced by source paper "hierarchical discrete distribution decomposition for match density estimation"):
  with members:
    - "lightweight probabilistic deep networks"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "segstereo exploiting semantic information for disparity estimation"
    - "layer structured 3d scene inference via view synthesis"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "pyramid stereo matching network"

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)