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: "recurrent transformer networks for semantic correspondence"
  Abstract: We present recurrent transformer networks (RTNs) for obtaining dense correspondences between semantically similar images. Our networks accomplish this through an iterative process of estimating spatial transformations between the input images and using these transformations to generate aligned convolutional activations. By directly estimating the transformations between an image pair, rather than employing spatial transformer networks to independently normalize each individual image, we show that greater accuracy can be achieved. This process is conducted in a recursive manner to refine both the transformation estimates and the feature representations. In addition, a technique is presented for weakly-supervised training of RTNs that is based on a proposed classification loss. With RTNs, state-of-the-art performance is attained on several benchmarks for semantic correspondence.

Connected through hyperedge HE-1 (induced by source paper "conditional single view shape generation for multi view stereo reconstruction"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "so net self organizing network for point cloud analysis"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "learning to reconstruct shapes from unseen classes"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "learning shape priors for single view 3d completion and reconstruction"

Connected through hyperedge HE-2 (induced by source paper "learning semantic correspondence exploiting an object level prior"):
  with members:
    - "sfnet learning object aware semantic correspondence"
    - "hyperpixel flow semantic correspondence with multi layer neural features"
    - "learning correspondence from the cycle consistency of time"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "neighbourhood consensus networks"
    - "attentive semantic alignment with offset aware correlation kernels"
    - "tracking emerges by colorizing videos"

Connected through hyperedge HE-3 (induced by source paper "glu net global local universal network for dense flow and correspondences"):
  with members:
    - "geometric image correspondence verification by dense pixel matching"
    - "dynamic context correspondence network for semantic alignment"
    - "an empirical evaluation study on the training of sdc features for dense pixel matching"
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "glampoints greedily learned accurate match points"
    - "semantic attribute matching networks"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "lf net learning local features from images"

Connected through hyperedge HE-4 (induced by source paper "show match and segment joint learning of semantic matching and object co segmentation"):
  with members:
    - "semi supervised semantic matching"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "multimodal unsupervised image to image translation"
    - "neighbourhood consensus networks"

Connected through hyperedge HE-5 (induced by source paper "hyperpixel flow semantic correspondence with multi layer neural features"):
  with members:
    - "semantic matching by weakly supervised 2d point set registration"
    - "exploring randomly wired neural networks for image recognition"
    - "parn pyramidal affine regression networks for dense semantic correspondence"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "neighbourhood consensus networks"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "attentive semantic alignment with offset aware correlation kernels"
    - "darts differentiable architecture search"

Connected through hyperedge HE-6 (induced by source paper "semantic attribute matching networks"):
  with members:
    - "neural best buddies sparse cross domain correspondence"
    - "a closed form solution to photorealistic image stylization"
    - "neighbourhood consensus networks"
    - "arbitrary style transfer with deep feature reshuffle"
    - "convolutional neural network architecture for geometric matching"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"

Connected through hyperedge HE-7 (induced by source paper "multi scale convolutions for learning context aware feature representations"):
  with members:
    - "parn pyramidal affine regression networks for dense semantic correspondence"
    - "end to end weakly supervised semantic alignment"
    - "convolutional neural network architecture for geometric matching"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "dctm discrete continuous transformation matching for semantic flow"
    - "deformable convolutional networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "fcss fully convolutional self similarity for dense semantic correspondence"

Connected through hyperedge HE-8 (induced by source paper "joint learning of semantic alignment and object landmark detection"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "neighbourhood consensus networks"
    - "attentive semantic alignment with offset aware correlation kernels"
    - "unsupervised discovery of object landmarks as structural representations"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "end to end weakly supervised semantic alignment"
    - "convolutional neural network architecture for geometric matching"

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)