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: "learning semantic segmentation with diverse supervision"
  Abstract: Models based on deep convolutional neural networks (CNN) have significantly improved the performance of semantic segmentation. However, learning these models requires a large amount of training images with pixel-level labels, which are very costly and time-consuming to collect. In this paper, we propose a method for learning CNN-based semantic segmentation models from images with several types of annotations that are available for various computer vision tasks, including image-level labels for classification, box-level labels for object detection and pixel-level labels for semantic segmentation. The proposed method is flexible and can be used together with any existing CNN-based semantic segmentation networks. Experimental evaluation on the challenging PASCAL VOC 2012 and SIFT-flow benchmarks demonstrate that the proposed method can effectively make use of diverse training data to improve the performance of the learned models.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive analysis of weakly supervised semantic segmentation in different image domains"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "dense fusion classmate network for land cover classification"
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "constrained cnn losses for weakly supervised segmentation"
    - "weakly supervised semantic segmentation of satellite images"
    - "weakly supervised instance segmentation using class peak response"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "on boosting semantic street scene segmentation with weak supervision"):
  with members:
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "a domain agnostic normalization layer for unsupervised adversarial domain adaptation"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "domain adaptation for visual applications a comprehensive survey"
    - "transfer learning for cross dataset recognition a survey"
    - "recent advances in transfer learning for cross dataset visual recognition a problem oriented perspective"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-3 (induced by source paper "data selection for training semantic segmentation cnns with cross dataset weak supervision"):
  with members:
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "on boosting semantic street scene segmentation with weak supervision"
    - "are all training examples created equal an empirical study"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "taskonomy disentangling task transfer learning"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "pixel level data augmentation for semantic image segmentation using generative adversarial networks"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (induced by source paper "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"):
  with members:
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"

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)