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: "situational object boundary detection"
  Abstract: Intuitively, the appearance of true object boundaries varies from image to image. Hence the usual monolithic approach of training a single boundary predictor and applying it to all images regardless of their content is bound to be suboptimal. In this paper we therefore propose situational object boundary detection: We first define a variety of situations and train a specialized object boundary detector for each of them using [Dollar and Zitnick 2013]. Then given a test image, we classify it into these situations using its context, which we model by global image appearance. We apply the corresponding situational object boundary detectors, and fuse them based on the classification probabilities. In experiments on ImageNet, Microsoft COCO, and Pascal VOC 2012 segmentation we show that our situational object boundary detection gives significant improvements over a monolithic approach. Additionally, our method substantially outperforms [Hariharan et al. 2011] on semantic contour detection on their SBD dataset.

Connected through hyperedge HE-1 (induced by source paper "look at boundary a boundary aware face alignment algorithm"):
  with members:
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "densereg fully convolutional dense shape regression in the wild"
    - "style aggregated network for facial landmark detection"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "casenet deep category aware semantic edge detection"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"

Connected through hyperedge HE-2 (induced by source paper "the devil is in the decoder classification regression and gans"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "improved training of wasserstein gans"
    - "super resolution with deep adaptive image resampling"
    - "rethinking atrous convolution for semantic image segmentation"
    - "stacked hourglass networks for human pose estimation"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-3 (induced by source paper "convolutional oriented boundaries from image segmentation to high level tasks"):
  with members:
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "deep retinal image understanding"
    - "convolutional oriented boundaries"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "ssd single shot multibox detector"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "learning to refine object segments"
    - "what makes for effective detection proposals"

Connected through hyperedge HE-4 (induced by source paper "video object segmentation without temporal information"):
  with members:
    - "the 2017 davis challenge on video object segmentation"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "instance sensitive fully convolutional networks"
    - "learning motion patterns in videos"
    - "ssd single shot multibox detector"
    - "object contour detection with a fully convolutional encoder decoder network"

Connected through hyperedge HE-5 (induced by source paper "semantically guided video object segmentation"):
  with members:
    - "a multipath network for object detection"
    - "fully convolutional instance aware semantic segmentation"
    - "holistically nested edge detection"
    - "iterative instance segmentation"
    - "instance aware semantic segmentation via multi task network cascades"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "recurrent instance segmentation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "object contour detection with a fully convolutional encoder decoder network"):
  with members:
    - "what makes for effective detection proposals"
    - "semantic image segmentation via deep parsing network"
    - "learning to segment object candidates"
    - "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"
    - "conditional random fields as recurrent neural networks"
    - "fast r cnn"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised object boundaries"):
  with members:
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "what makes for effective detection proposals"
    - "holistically nested edge detection"
    - "unsupervised learning of edges"
    - "high for low and low for high efficient boundary detection from deep object features and its applications to high level vision"
    - "segdeepm exploiting segmentation and context in deep neural networks for object detection"
    - "fast r cnn"
    - "semantic segmentation with boundary neural fields"

Connected through hyperedge HE-8 (induced by source paper "pascal boundaries a class agnostic semantic boundary dataset"):
  with members:
    - "semantic amodal segmentation"
    - "holistically nested edge detection"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"
    - "deepedge a multi scale bifurcated deep network for top down contour detection"
    - "microsoft coco common objects in context"
    - "fast edge detection using structured forests"
    - "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)