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: "oriented edge forests for boundary detection"
  Abstract: We present a simple, efficient model for learning boundary detection based on a random forest classifier. Our approach combines (1) efficient clustering of training examples based on simple partitioning of the space of local edge orientations and (2) scale-dependent calibration of individual tree output probabilities prior to multiscale combination. The resulting model outperforms published results on the challenging BSDS500 boundary detection benchmark. Further, on large datasets our model requires substantially less memory for training and speeds up training time by a factor of 10 over the structured forest model.

Connected through hyperedge HE-1 (induced by source paper "red net a recursive encoder decoder network for edge detection"):
  with members:
    - "casenet deep category aware semantic edge detection"
    - "richer convolutional features for edge detection"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "densely connected convolutional networks"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "deep hierarchical parsing for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "bi directional cascade network for perceptual edge detection"):
  with members:
    - "learning deep structured multi scale features using attention gated crfs for contour prediction"
    - "learning to predict crisp boundaries"
    - "richer convolutional features for edge detection"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "multi scale dense networks for resource efficient image classification"
    - "srn side output residual network for object symmetry detection in the wild"
    - "hierarchical multi scale attention networks for action recognition"
    - "convolutional oriented boundaries from image segmentation to high level tasks"

Connected through hyperedge HE-3 (induced by source paper "adaptive morphological reconstruction for seeded image segmentation"):
  with members:
    - "improved image segmentation via cost minimization of multiple hypotheses"
    - "image segmentation using sparse subset selection"
    - "revisiting graph construction for fast image segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "conditional random fields as recurrent neural networks"
    - "superpixel convolutional networks using bilateral inceptions"
    - "fast edge detection using structured forests"

Connected through hyperedge HE-4 (induced by source paper "learning deep structured multi scale features using attention gated crfs for contour prediction"):
  with members:
    - "learning cross modal deep representations for robust pedestrian detection"
    - "richer convolutional features for edge detection"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "crafting gbd net for object detection"
    - "crf cnn modeling structured information in human pose estimation"
    - "semicontour a semi supervised learning approach for contour detection"

Connected through hyperedge HE-5 (induced by source paper "learning to predict crisp boundaries"):
  with members:
    - "casenet deep category aware semantic edge detection"
    - "aggregated residual transformations for deep neural networks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "learning to refine object segments"
    - "holistically nested edge detection"
    - "high for low and low for high efficient boundary detection from deep object features and its applications to high level vision"

Connected through hyperedge HE-6 (induced by source paper "rotdcf decomposition of convolutional filters for rotation equivariant deep networks"):
  with members:
    - "learning steerable filters for rotation equivariant cnns"
    - "oriented response networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "harmonic networks deep translation and rotation equivariance"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "group equivariant convolutional networks"
    - "convolutional neural networks with low rank regularization"

Connected through hyperedge HE-7 (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"
    - "learning to extract motion from videos in convolutional neural networks"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "richer convolutional features for edge detection"):
  with members:
    - "deep crisp boundaries from boundaries to higher level tasks"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "hi fi hierarchical feature integration for skeleton detection"
    - "webseg learning semantic segmentation from web searches"
    - "learning deep structured multi scale features using attention gated crfs for contour prediction"
    - "casenet deep category aware semantic edge detection"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "convolutional oriented boundaries"

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)