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 weakly supervised learning methods for classification and localization in histology images a survey"
  Abstract: Using state-of-the-art deep learning models for the computer-assisted diagnosis of diseases like cancer raises several challenges related to the nature and availability of labeled histology images. In particular, cancer grading and localization in these images normally relies on both image- and pixel-level labels, the latter requiring a costly annotation process. In this survey, deep weakly-supervised learning (WSL) architectures are investigated to identify and locate diseases in histology image, without the need for pixel-level annotations. Given a training dataset with globally-annotated images, these models allow to simultaneously classify histology images, while localizing the corresponding regions of interest. These models are organized into two main approaches -- (1) bottom-up approaches (based on forward-pass information through a network, either by spatial pooling of representations/scores, or by detecting class regions), and (2) top-down approaches (based on backward-pass information within a network, inspired by human visual attention). Since relevant WSL models have mainly been developed in the computer vision community, and validated on natural scene images, we assess the extent to which they apply to histology images which have challenging properties, e.g., large size, non-salient and highly unstructured regions, stain heterogeneity, and coarse/ambiguous labels. The most relevant deep WSL models (e.g., CAM, WILDCAT and Deep MIL) are compared experimentally in terms of accuracy (classification and pixel-level localization) on several public benchmark histology datasets for breast and colon cancer (BACH ICIAR 2018, BreakHis, CAMELYON16, and GlaS). Results indicate that several deep learning models, and in particular WILDCAT and deep MIL can provide a high level of classification accuracy, although pixel-wise localization of cancer regions remains an issue for such 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"
    - "imposing hard constraints on deep networks promises and limitations"
    - "deep label distribution learning with label ambiguity"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "learning to play in a day faster deep reinforcement learning by optimality tightening"

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)