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 learning with mixed supervision for brain tumor segmentation"
  Abstract: Most of the current state-of-the-art methods for tumor segmentation are based on machine learning models trained on manually segmented images. This type of training data is particularly costly, as manual delineation of tumors is not only time-consuming but also requires medical expertise. On the other hand, images with a provided global label (indicating presence or absence of a tumor) are less informative but can be obtained at a substantially lower cost. In this paper, we propose to use both types of training data (fully-annotated and weakly-annotated) to train a deep learning model for segmentation. The idea of our approach is to extend segmentation networks with an additional branch performing image-level classification. The model is jointly trained for segmentation and classification tasks in order to exploit information contained in weakly-annotated images while preventing the network to learn features which are irrelevant for the segmentation task. We evaluate our method on the challenging task of brain tumor segmentation in Magnetic Resonance images from BRATS 2018 challenge. We show that the proposed approach provides a significant improvement of segmentation performance compared to the standard supervised learning. The observed improvement is proportional to the ratio between weakly-annotated and fully-annotated images available for training.

Connected through hyperedge HE-1 (induced by source paper "a multi task u net for segmentation with lazy labels"):
  with members:
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "coarse to fine semantic segmentation from image level labels"
    - "a two stage 3d unet framework for multi class segmentation on full resolution image"
    - "coarse to fine volumetric segmentation of teeth in cone beam ct"
    - "multiclass weighted loss for instance segmentation of cluttered cells"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "saliency guided deep network for weakly supervised image segmentation"

Connected through hyperedge HE-2 (induced by source paper "mixed supervised dual network for medical image segmentation"):
  with members:
    - "constrained cnn losses for weakly supervised segmentation"
    - "annotation cost minimization for medical image segmentation using suggestive mixed supervision fully convolutional networks"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"

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)