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: "semantic segmentation with reverse attention"
  Abstract: Recent development in fully convolutional neural network enables efficient end-to-end learning of semantic segmentation. Traditionally, the convolutional classifiers are taught to learn the representative semantic features of labeled semantic objects. In this work, we propose a reverse attention network (RAN) architecture that trains the network to capture the opposite concept (i.e., what are not associated with a target class) as well. The RAN is a three-branch network that performs the direct, reverse and reverse-attention learning processes simultaneously. Extensive experiments are conducted to show the effectiveness of the RAN in semantic segmentation. Being built upon the DeepLabv2-LargeFOV, the RAN achieves the state-of-the-art mIoU score (48.1%) for the challenging PASCAL-Context dataset. Significant performance improvements are also observed for the PASCAL-VOC, Person-Part, NYUDv2 and ADE20K datasets.

Connected through hyperedge HE-1 (induced by source paper "on guiding video object segmentation"):
  with members:
    - "focal loss for dense object detection"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "learning video object segmentation with visual memory"
    - "saliency weighted convolutional features for instance search"
    - "video propagation networks"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "reverse attention for salient object detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "learning to refine object contours with a top down fully convolutional encoder decoder network"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "squeeze and excitation networks"
    - "srn side output residual network for object symmetry detection in the wild"
    - "instance level salient object segmentation"

Connected through hyperedge HE-3 (induced by source paper "tree structured kronecker convolutional network for semantic segmentation"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cgnet a light weight context guided network for semantic segmentation"
    - "context encoding for semantic segmentation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "foveanet perspective aware urban scene parsing"

Connected through hyperedge HE-4 (induced by source paper "unsupervised video object segmentation with distractor aware online adaptation"):
  with members:
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised hard example mining from videos for improved object detection"
    - "videomatch matching based video object segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "design pseudo ground truth with motion cue for unsupervised video object segmentation"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "video object segmentation without temporal information"
    - "learning video object segmentation with visual memory"

Connected through hyperedge HE-5 (induced by source paper "instance embedding transfer to unsupervised video object segmentation"):
  with members:
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "learning video object segmentation with visual memory"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "segflow joint learning for video object segmentation and optical flow"
    - "semantic instance segmentation via deep metric learning"
    - "semantic instance segmentation with a discriminative loss function"
    - "sfm net learning of structure and motion from video"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "design pseudo ground truth with motion cue for unsupervised video object segmentation"):
  with members:
    - "instance embedding transfer to unsupervised video object segmentation"
    - "video object segmentation with re identification"
    - "lucid data dreaming for video object segmentation"
    - "learning video object segmentation with visual memory"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "segflow joint learning for video object segmentation and optical flow"
    - "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"
    - "mask r cnn"

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)