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: "constrained deep weak supervision for histopathology image segmentation"
  Abstract: In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm are threefold: 1) we build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCNs) in which image-to-image weakly-supervised learning is performed; 2) we develop a DWS formulation to exploit multi-scale learning under weak supervision within FCNs; and 3) constraints about positive instances are introduced in our approach to effectively explore additional weakly supervised information that is easy to obtain and enjoy a significant boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL, is easy to implement and can be trained efficiently. Our system demonstrates the state-of-the-art results on large-scale histopathology image data sets and can be applied to various applications in medical imaging beyond histopathology images, such as MRI, CT, and ultrasound images.

Connected through hyperedge HE-1 (induced by source paper "non parametric uni modality constraints for deep ordinal classification"):
  with members:
    - "constrained deep networks lagrangian optimization via log barrier extensions"
    - "ordinal distribution regression for gait based age estimation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "min max entropy for weakly supervised pointwise localization"
    - "deep weakly supervised learning methods for classification and localization in histology images a survey"
    - "imposing hard constraints on deep networks promises and limitations"
    - "deep label distribution learning with label ambiguity"
    - "learning to play in a day faster deep reinforcement learning by optimality tightening"

Connected through hyperedge HE-2 (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-3 (induced by source paper "constrained cnn losses for weakly supervised segmentation"):
  with members:
    - "constrained deep learning using conditional gradient and applications in computer vision"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "imposing hard constraints on deep networks promises and limitations"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "semantic transferable weakly supervised endoscopic lesions segmentation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "weakly supervised deep learning for thoracic disease classification and localization on chest x rays"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "deep learning models for digital pathology"):
  with members:
    - "two stage convolutional neural network for breast cancer histology image classification"
    - "inferring a third spatial dimension from 2d histological images"
    - "deep learning based assessment of tumor associated stroma for diagnosing breast cancer in histopathology images"
    - "context based normalization of histological stains using deep convolutional features"
    - "context aware stacked convolutional neural networks for classification of breast carcinomas in whole slide histopathology images"
    - "representation aggregation networks for segmentation of multi gigapixel histology images"
    - "detecting cancer metastases on gigapixel pathology images"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "deep weakly supervised learning methods for classification and localization in histology images a survey"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "weakly supervised object localization using min max entropy an interpretable framework"
    - "high resolution representations for labeling pixels and regions"
    - "attention based deep multiple instance learning"
    - "deep self taught learning for weakly supervised object localization"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "multiple instance detection network with online instance classifier refinement"

Connected through hyperedge HE-7 (induced by source paper "constrained deep networks lagrangian optimization via log barrier extensions"):
  with members:
    - "constrained cnn losses for weakly supervised segmentation"
    - "constrained deep learning using conditional gradient and applications in computer vision"
    - "a curriculum domain adaptation approach to the semantic segmentation of urban scenes"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "beyond gradient descent for regularized segmentation losses"
    - "imposing hard constraints on deep networks promises and limitations"

Connected through hyperedge HE-8 (induced by source paper "discretely constrained deep network for weakly supervised segmentation"):
  with members:
    - "prior aware neural network for partially supervised multi organ segmentation"
    - "curriculum semi supervised segmentation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "beyond gradient descent for regularized segmentation losses"
    - "predicting deeper into the future of semantic segmentation"
    - "unbiased shape compactness for segmentation"
    - "discrete continuous admm for transductive inference in higher order mrfs"
    - "dope distributed optimization for pairwise energies"

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)