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: "multiple instance learning for heterogeneous images training a cnn for histopathology"
  Abstract: Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an image-level classification by using the quantile function. The quantile function provides a more complete description of the heterogeneity within each image, improving image-level classification. We also adapt image augmentation to the MI framework by randomly selecting cropped regions on which to apply MI aggregation during each epoch of training. This provides a mechanism to study the importance of MI learning. We validate our method on five different classification tasks for breast tumor histology and provide a visualization method for interpreting local image classifications that could lead to future insights into tumor heterogeneity.

Connected through hyperedge HE-1 (induced by source paper "multi scale domain adversarial multiple instance cnn for cancer subtype classification with non annotated histopathological images"):
  with members:
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"
    - "attention based deep multiple instance learning"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "xception deep learning with depthwise separable convolutions"
    - "patch based convolutional neural network for whole slide tissue image classification"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "a survey on explainable artificial intelligence xai towards medical xai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable neural networks for predicting mortality risk using multi modal electronic health records"
    - "an evaluation of the human interpretability of explanation"
    - "protoattend attention based prototypical learning"
    - "understanding the un interpretability of natural image distributions using generative models"
    - "outlining the design space of explainable intelligent systems for medical diagnosis"
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "interpretable dynamics models for data efficient reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "deep instance level hard negative mining model for histopathology images"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "attention based deep multiple instance learning"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "deep learning based assessment of tumor associated stroma for diagnosing breast cancer in histopathology images"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "training region based object detectors with online hard example mining"
    - "multiple instance learning convolutional neural networks for object recognition"

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)