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: "dctm discrete continuous transformation matching for semantic flow"
  Abstract: Techniques for dense semantic correspondence have provided limited ability to deal with the geometric variations that commonly exist between semantically similar images. While variations due to scale and rotation have been examined, there lack practical solutions for more complex deformations such as affine transformations because of the tremendous size of the associated solution space. To address this problem, we present a discrete-continuous transformation matching (DCTM) framework where dense affine transformation fields are inferred through a discrete label optimization in which the labels are iteratively updated via continuous regularization. In this way, our approach draws solutions from the continuous space of affine transformations in a manner that can be computed efficiently through constant-time edge-aware filtering and a proposed affine-varying CNN-based descriptor. Experimental results show that this model outperforms the state-of-the-art methods for dense semantic correspondence on various benchmarks.

Connected through hyperedge HE-1 (induced by source paper "a two stream symmetric network with bidirectional ensemble for aerial image matching"):
  with members:
    - "attentive semantic alignment with offset aware correlation kernels"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "sniper efficient multi scale training"
    - "end to end weakly supervised semantic alignment"
    - "convolutional neural network architecture for geometric matching"
    - "squeeze and excitation networks"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "scnet learning semantic correspondence"

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"
    - "recurrent transformer networks for semantic correspondence"
    - "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"

Connected through hyperedge HE-3 (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"
    - "recurrent transformer networks for semantic correspondence"
    - "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"

Connected through hyperedge HE-4 (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"
    - "recurrent transformer networks for 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"

Connected through hyperedge HE-5 (induced by source paper "sfnet learning object aware semantic correspondence"):
  with members:
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "attentive semantic alignment with offset aware correlation kernels"
    - "end to end weakly supervised semantic alignment"
    - "convolutional neural network architecture for geometric matching"
    - "improving landmark localization with semi supervised learning"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"

Connected through hyperedge HE-6 (induced by source paper "multi scale convolutions for learning context aware feature representations"):
  with members:
    - "parn pyramidal affine regression networks for dense semantic correspondence"
    - "recurrent transformer networks for 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"
    - "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-7 (induced by source paper "dynamic context correspondence network for semantic alignment"):
  with members:
    - "parn pyramidal affine regression networks for dense semantic correspondence"
    - "recurrent transformer networks for semantic correspondence"
    - "neighbourhood consensus networks"
    - "attentive semantic alignment with offset aware correlation kernels"
    - "end to end weakly supervised semantic alignment"
    - "non local neural networks"
    - "convolutional neural network architecture for geometric matching"
    - "understanding the effective receptive field in deep convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "attentive semantic alignment with offset aware correlation kernels"):
  with members:
    - "convolutional neural network architecture for geometric matching"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "scnet learning semantic correspondence"
    - "text guided attention model for image captioning"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "large scale image retrieval with attentive deep local features"
    - "learning dense correspondence via 3d guided cycle consistency"

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)