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: "crf learning with cnn features for image segmentation"
  Abstract: Conditional Random Rields (CRF) have been widely applied in image segmentations. While most studies rely on hand-crafted features, we here propose to exploit a pre-trained large convolutional neural network (CNN) to generate deep features for CRF learning. The deep CNN is trained on the ImageNet dataset and transferred to image segmentations here for constructing potentials of superpixels. Then the CRF parameters are learnt using a structured support vector machine (SSVM). To fully exploit context information in inference, we construct spatially related co-occurrence pairwise potentials and incorporate them into the energy function. This prefers labelling of object pairs that frequently co-occur in a certain spatial layout and at the same time avoids implausible labellings during the inference. Extensive experiments on binary and multi-class segmentation benchmarks demonstrate the promise of the proposed method. We thus provide new baselines for the segmentation performance on the Weizmann horse, Graz-02, MSRC-21, Stanford Background and PASCAL VOC 2011 datasets.

Connected through hyperedge HE-1 (induced by source paper "unsupervised visual feature learning with spike timing dependent plasticity how far are we from traditional feature learning approaches"):
  with members:
    - "deep learning in spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "deep patch learning for weakly supervised object classification and discovery"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "training deep spiking neural networks using backpropagation"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "unsupervised feature learning with c svddnet"

Connected through hyperedge HE-2 (induced by source paper "person re identification with deep similarity guided graph neural network"):
  with members:
    - "end to end deep kronecker product matching for person re identification"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "harmonious attention network for person re identification"
    - "multi channel pyramid person matching network for person re identification"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "deep group shuffling random walk for person re identification"
    - "vip cnn visual phrase guided convolutional neural network"
    - "random erasing data augmentation"

Connected through hyperedge HE-3 (induced by source paper "taskonomy disentangling task transfer learning"):
  with members:
    - "gibson env real world perception for embodied agents"
    - "transitive invariance for self supervised visual representation learning"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "multi task self supervised visual learning"
    - "matterport3d learning from rgb d data in indoor environments"
    - "representation learning by learning to count"
    - "playing for benchmarks"

Connected through hyperedge HE-4 (induced by source paper "leveraging structural context models and ranking score fusion for human interaction prediction"):
  with members:
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "beyond short snippets deep networks for video classification"
    - "learning temporal embeddings for complex video analysis"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-5 (induced by source paper "image distortion detection using convolutional neural network"):
  with members:
    - "on the use of deep learning for blind image quality assessment"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "you only look once unified real time object detection"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "scikit image image processing in python"

Connected through hyperedge HE-6 (induced by source paper "gland instance segmentation using deep multichannel neural networks"):
  with members:
    - "instance sensitive fully convolutional networks"
    - "object detection free instance segmentation with labeling transformations"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gland instance segmentation by deep multichannel side supervision"
    - "learning to refine object segments"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "learning to segment object candidates"

Connected through hyperedge HE-7 (induced by source paper "discriminative training of deep fully connected continuous crfs with task specific loss"):
  with members:
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "deeply learning the messages in message passing inference"
    - "conditional random fields as recurrent neural networks"
    - "efficient piecewise training of deep structured models for semantic segmentation"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "learning rich features from rgb d images for object detection and segmentation"

Connected through hyperedge HE-8 (induced by source paper "revisiting graph construction for fast image segmentation"):
  with members:
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "object skeleton extraction in natural images by fusing scale associated deep side outputs"
    - "semicontour a semi supervised learning approach for contour detection"
    - "unsupervised learning of edges"
    - "learning deconvolution network for semantic segmentation"

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)