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: "transferable semi supervised semantic segmentation"
  Abstract: The performance of deep learning based semantic segmentation models heavily depends on sufficient data with careful annotations. However, even the largest public datasets only provide samples with pixel-level annotations for rather limited semantic categories. Such data scarcity critically limits scalability and applicability of semantic segmentation models in real applications. In this paper, we propose a novel transferable semi-supervised semantic segmentation model that can transfer the learned segmentation knowledge from a few strong categories with pixel-level annotations to unseen weak categories with only image-level annotations, significantly broadening the applicable territory of deep segmentation models. In particular, the proposed model consists of two complementary and learnable components: a Label transfer Network (L-Net) and a Prediction transfer Network (P-Net). The L-Net learns to transfer the segmentation knowledge from strong categories to the images in the weak categories and produces coarse pixel-level semantic maps, by effectively exploiting the similar appearance shared across categories. Meanwhile, the P-Net tailors the transferred knowledge through a carefully designed adversarial learning strategy and produces refined segmentation results with better details. Integrating the L-Net and P-Net achieves 96.5% and 89.4% performance of the fully-supervised baseline using 50% and 0% categories with pixel-level annotations respectively on PASCAL VOC 2012. With such a novel transfer mechanism, our proposed model is easily generalizable to a variety of new categories, only requiring image-level annotations, and offers appealing scalability in real applications.

Connected through hyperedge HE-1 (induced by source paper "transferable semi supervised 3d object detection from rgb d data"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "weakly supervised instance segmentation using class peak response"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "weakly and semi supervised panoptic segmentation"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "an implementation of faster rcnn with study for region sampling"

Connected through hyperedge HE-2 (induced by source paper "self produced guidance for weakly supervised object localization"):
  with members:
    - "few example object detection with model communication"
    - "deeply supervised salient object detection with short connections"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "adversarial complementary learning for weakly supervised object localization"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "macro micro adversarial network for human parsing"
    - "deep self taught learning for weakly supervised object localization"

Connected through hyperedge HE-3 (induced by source paper "deep semi supervised segmentation with weight averaged consistency targets"):
  with members:
    - "spinal cord gray matter segmentation using deep dilated convolutions"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "semi supervised deep learning for fully convolutional networks"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "a survey on deep learning in medical image analysis"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"
    - "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)