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: "pascal boundaries a class agnostic semantic boundary dataset"
  Abstract: In this paper, we address the boundary detection task motivated by the ambiguities in current definition of edge detection. To this end, we generate a large database consisting of more than 10k images (which is 20 bigger than existing edge detection databases) along with ground truth boundaries between 459 semantic classes including both foreground objects and different types of background, and call it the PASCAL Boundaries dataset, which will be released to the community. In addition, we propose a novel deep network-based multi-scale semantic boundary detector and name it Multi-scale Deep Semantic Boundary Detector (M-DSBD). We provide baselines using models that were trained on edge detection and show that they transfer reasonably to the task of boundary detection. Finally, we point to various important research problems that this dataset can be used for.

Connected through hyperedge HE-1 (induced by source paper "adversarial examples for edge detection they exist and they transfer"):
  with members:
    - "harmonic adversarial attack method"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "generating natural language adversarial examples"
    - "intermediate level adversarial attack for enhanced transferability"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "countering adversarial images using input transformations"
    - "generative adversarial perturbations"

Connected through hyperedge HE-2 (induced by source paper "holistically nested edge detection"):
  with members:
    - "convolutional oriented boundaries"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "object skeleton extraction in natural images by fusing scale associated deep side outputs"
    - "dense volume to volume vascular boundary detection"
    - "weakly supervised object boundaries"
    - "unsupervised learning of edges"
    - "pixel wise deep learning for contour detection"
    - "semantic image segmentation with task specific edge detection using cnns and a discriminatively trained domain transform"

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)