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: "deep co training for semi supervised image segmentation"
  Abstract: In this paper, we aim to improve the performance of semantic image segmentation in a semi-supervised setting in which training is effectuated with a reduced set of annotated images and additional non-annotated images. We present a method based on an ensemble of deep segmentation models. Each model is trained on a subset of the annotated data, and uses the non-annotated images to exchange information with the other models, similar to co-training. Even if each model learns on the same non-annotated images, diversity is preserved with the use of adversarial samples. Our results show that this ability to simultaneously train models, which exchange knowledge while preserving diversity, leads to state-of-the-art results on two challenging medical image datasets.

Connected through hyperedge HE-1 (induced by source paper "revisiting cyclegan for semi supervised segmentation"):
  with members:
    - "constrained cnn losses for weakly supervised segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "adversarial learning for semi supervised semantic segmentation"
    - "unsupervised domain adaptation for medical imaging segmentation with self ensembling"
    - "semi supervised deep learning for fully convolutional networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "lidar camera co training for semi supervised road detection"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "progressive lidar adaptation for road detection"
    - "lidar camera fusion for road detection using fully convolutional neural networks"
    - "deep co training for semi supervised image recognition"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "temporal ensembling for semi supervised learning"
    - "u net convolutional networks for biomedical image 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)