human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "chest x rays classification a multi label and fine grained problem"):
  Abstract: The widely used ChestX-ray14 dataset addresses an important medical image classification problem and has the following caveats: 1) many lung pathologies are visually similar, 2) a variant of diseases including lung cancer, tuberculosis, and pneumonia are present in a single scan, i.e. multiple labels and 3) The incidence of healthy images is much larger than diseased samples, creating imbalanced data. These properties are common in medical domain. Existing literature uses stateof- the-art DensetNet/Resnet models being transfer learned where output neurons of the networks are trained for individual diseases to cater for multiple diseases labels in each image. However, most of them don't consider relationship between multiple classes. In this work we have proposed a novel error function, Multi-label Softmax Loss (MSML), to specifically address the properties of multiple labels and imbalanced data. Moreover, we have designed deep network architecture based on fine-grained classification concept that incorporates MSML. We have evaluated our proposed method on various network backbones and showed consistent performance improvements of AUC-ROC scores on the ChestX-ray14 dataset. The proposed error function provides a new method to gain improved performance across wider medical datasets.

Includes members:
  Member "learning to diagnose from scratch by exploiting dependencies among labels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic graph correlation learning for disease diagnosis with incomplete labels")
  Member "chexnet radiologist level pneumonia detection on chest x rays with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the future of digital health with federated learning")
  Member "improved bilinear pooling with cnns":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "exploiting temporal information for dcnn based fine grained object classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an open source c implementation of multi threaded gaussian mixture models k means and expectation maximisation")
  Member "learning to read chest x rays recurrent neural cascade model for automated image annotation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chexplain enabling physicians to explore and understanddata driven ai enabled medical imaging analysis")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)