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: "dense correspondences across scenes and scales"
  Abstract: We seek a practical method for establishing dense correspondences between two images with similar content, but possibly different 3D scenes. One of the challenges in designing such a system is the local scale differences of objects appearing in the two images. Previous methods often considered only few image pixels; matching only pixels for which stable scales may be reliably estimated. Recently, others have considered dense correspondences, but with substantial costs associated with generating, storing and matching scale invariant descriptors. Our work is motivated by the observation that pixels in the image have contexts—the pixels around them—which may be exploited in order to reliably estimate local scales. We make the following contributions. (i) We show that scales estimated in sparse interest points may be propagated to neighboring pixels where this information cannot be reliably determined. Doing so allows scale invariant descriptors to be extracted anywhere in the image. (ii) We explore three means for propagating this information: using the scales at detected interest points, using the underlying image information to guide scale propagation in each image separately, and using both images together. Finally, (iii), we provide extensive qualitative and quantitative results, demonstrating that scale propagation allows for accurate dense correspondences to be obtained even between very different images, with little computational costs beyond those required by existing methods.

Connected through hyperedge HE-1 (induced by source paper "dense scale selection over space time and space time"):
  with members:
    - "temporal scale selection in time causal scale space"

Connected through hyperedge HE-2 (induced by source paper "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"):
  with members:
    - "fully trainable deep matching"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "vqa visual question answering"
    - "dense semantic correspondence where every pixel is a classifier"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-3 (induced by source paper "scnet learning semantic correspondence"):
  with members:
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "lift learned invariant feature transform"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "what makes for effective detection proposals"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "computing the stereo matching cost with a convolutional neural network"
    - "dense semantic correspondence where every pixel is a classifier"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-4 (induced by source paper "proposal flow semantic correspondences from object proposals"):
  with members:
    - "learning dense correspondence via 3d guided cycle consistency"
    - "contextual action recognition with r cnn"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "deepmatching hierarchical deformable dense matching"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning to compare image patches via convolutional neural networks"
    - "multi image matching via fast alternating minimization"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-5 (induced by source paper "temporal tessellation a unified approach for video analysis"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "summary transfer exemplar based subset selection for video summarization"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "video summarization with long short term memory"
    - "end to end learning of action detection from frame glimpses in videos"
    - "rnn fisher vectors for action recognition and image annotation"
    - "unsupervised learning of video representations using lstms"
    - "a dataset for movie description"

Connected through hyperedge HE-6 (induced by source paper "the cuda latch binary descriptor because sometimes faster means better"):
  with members:
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "latch learned arrangements of three patch codes"
    - "learning to compare image patches via convolutional neural networks"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-7 (induced by source paper "latch learned arrangements of three patch codes"):
  with members:
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "domain size pooling in local descriptors dsp sift"):
  with members:
    - "visual scene representations contrast scaling and occlusion"
    - "return of the devil in the details delving deep into convolutional nets"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "descriptor matching with convolutional neural networks a comparison to sift"
    - "on the design and analysis of multiple view descriptors"
    - "unsupervised feature learning by augmenting single images"
    - "scene parsing with multiscale feature learning purity trees and optimal covers"
    - "steps towards a theory of visual information active perception signal to symbol conversion and the interplay between sensing and control"

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)