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: "classifying and segmenting microscopy images with deep multiple instance learning"
  Abstract: Motivation: High-content screening (HCS) technologies have enabled large scale imaging experiments for studying cell biology and for drug screening. These systems produce hundreds of thousands of microscopy images per day and their utility depends on automated image analysis. Recently, deep learning approaches that learn feature representations directly from pixel intensity values have dominated object recognition challenges. These tasks typically have a single centered object per image and existing models are not directly applicable to microscopy datasets. Here we develop an approach that combines deep convolutional neural networks (CNNs) with multiple instance learning (MIL) in order to classify and segment microscopy images using only whole image level annotations.#R##N##R##N#Results: We introduce a new neural network architecture that uses MIL to simultaneously classify and segment microscopy images with populations of cells. We base our approach on the similarity between the aggregation function used in MIL and pooling layers used in CNNs. To facilitate aggregating across large numbers of instances in CNN feature maps we present the Noisy-AND pooling function, a new MIL operator that is robust to outliers. Combining CNNs with MIL enables training CNNs using whole microscopy images with image level labels. We show that training end-to-end MIL CNNs outperforms several previous methods on both mammalian and yeast datasets without requiring any segmentation steps.#R##N##R##N#Availability and implementation: Torch7 implementation available upon request.#R##N##R##N#Contact: ac.otnorotu.liam@suark.nero

Connected through hyperedge HE-1 (induced by source paper "learning from web data with self organizing memory module"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "learning from web data the benefit of unsupervised object localization"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "attention based deep multiple instance learning"
    - "joint optimization framework for learning with noisy labels"

Connected through hyperedge HE-2 (induced by source paper "localization of critical findings in chest x ray without local annotations using multi instance learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mimic cxr jpg a large publicly available database of labeled chest radiographs"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "unmasking clever hans predictors and assessing what machines really learn"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining the unique nature of individual gait patterns with deep learning"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "visualizing and understanding atari agents"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "learning from web data with memory module"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "attention based deep multiple instance learning"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "learning from noisy web data with category level supervision"
    - "learning to learn from noisy labeled data"

Connected through hyperedge HE-5 (induced by source paper "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection"):
  with members:
    - "sound event detection using graph laplacian regularization based on event co occurrence"
    - "weakly labelled audioset tagging with attention neural networks"
    - "sonyc a system for the monitoring analysis and mitigation of urban noise pollution"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "attention based deep multiple instance learning"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks"
    - "adaptive pooling operators for weakly labeled sound event detection"

Connected through hyperedge HE-6 (induced by source paper "on the feasibility of transfer learning code smells using deep learning"):
  with members:
    - "a machine learning based ensemble method for anti patterns detection"
    - "lstm a search space odyssey"
    - "a survey of machine learning for big code and naturalness"
    - "a syntactic neural model for general purpose code generation"
    - "easy over hard a case study on deep learning"
    - "latent predictor networks for code generation"
    - "sk_p a neural program corrector for moocs"
    - "a convolutional attention network for extreme summarization of source code"

Connected through hyperedge HE-7 (induced by source paper "kekulescope prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images"):
  with members:
    - "deep confidence a computationally efficient framework for calculating reliable prediction errors for deep neural networks"
    - "hydranet data augmentation for regression neural networks"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "chemi net a graph convolutional network for accurate drug property prediction"
    - "improving deep learning using generic data augmentation"
    - "molecular de novo design through deep reinforcement learning"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "moleculenet a benchmark for molecular machine learning"

Connected through hyperedge HE-8 (induced by source paper "multi attention networks for temporal localization of video level labels"):
  with members:
    - "attention based deep multiple instance learning"
    - "learnable pooling with context gating for video classification"
    - "the monkeytyping solution to the youtube 8m video understanding challenge"
    - "temporal modeling approaches for large scale youtube 8m video understanding"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "language modeling with gated convolutional networks"
    - "youtube 8m a large scale video classification benchmark"
    - "netvlad cnn architecture for weakly supervised place 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)