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: "boundary sensitive network for portrait segmentation"
  Abstract: Compared to the general semantic segmentation problem, portrait segmentation has higher precision requirement on boundary area. However, this problem has not been well studied in previous works. In this paper, we propose a boundary-sensitive deep neural network (BSN) for portrait segmentation. BSN introduces three novel techniques. First, an individual boundary-sensitive kernel is proposed by dilating the contour line and assigning the boundary pixels with multi-class labels. Second, a global boundary-sensitive kernel is employed as a position sensitive prior to further constrain the overall shape of the segmentation map. Third, we train a boundary-sensitive attribute classifier jointly with the segmentation network to reinforce the network with semantic boundary shape information. We have evaluated BSN on the current largest public portrait segmentation dataset,~\ie, the PFCN dataset, as well as the portrait images collected from other three popular image segmentation datasets: COCO, COCO-Stuff, and PASCAL VOC. Our method achieves the superior quantitative and qualitative performance over state-of-the-arts on all the datasets, especially on the boundary area.

Connected through hyperedge HE-1 (induced by source paper "sinet extreme lightweight portrait segmentation networks with spatial squeeze modules and information blocking decoder"):
  with members:
    - "asymmetric non local neural networks for semantic segmentation"
    - "searching for mobilenetv3"
    - "hbonet harmonious bottleneck on two orthogonal dimensions"
    - "extd extremely tiny face detector via iterative filter reuse"
    - "fast scnn fast semantic segmentation network"
    - "elastic improving cnns with dynamic scaling policies"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "c3 concentrated comprehensive convolution and its application to semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "boundary aware network for fast and high accuracy portrait segmentation"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "exfuse enhancing feature fusion for semantic segmentation"
    - "semantic human matting"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "fast deep matting for portrait animation on mobile phone"
    - "real time deep hair matting on mobile devices"
    - "deep image matting"

Connected through hyperedge HE-3 (induced by source paper "fast and light weight portrait segmentation"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "high quality correspondence and segmentation estimation for dual lens smart phone portraits"
    - "decomposeme simplifying convnets for end to end learning"
    - "multi scale context aggregation by dilated convolutions"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks 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)