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 learning with lung segmentation and bone shadow exclusion techniques for chest x ray analysis of lung cancer"
  Abstract: The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and nodules in lung cancer patients. Training and validation was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset, i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02), original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset after segmentation (dataset #04). The results demonstrate the high efficiency and usefulness of the considered pre-processing techniques in the simplified configuration even. The pre-processed dataset without bones (dataset #02) demonstrates the much better accuracy and loss results in comparison to the other pre-processed datasets after lung segmentation (datasets #02 and #03).

Connected through hyperedge HE-1 (induced by source paper "weakly supervised deep learning for thoracic disease classification and localization on chest x rays"):
  with members:
    - "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
    - "learning to recognize abnormalities in chest x rays with location aware dense networks"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "thoracic disease identification and localization with limited supervision"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"

Connected through hyperedge HE-2 (induced by source paper "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks"):
  with members:
    - "comparison of deep learning approaches for multi label chest x ray classification"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "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"
    - "boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "negbio a high performance tool for negation and uncertainty detection in radiology reports"

Connected through hyperedge HE-3 (induced by source paper "chest x ray analysis of tuberculosis by deep learning with segmentation and augmentation"):
  with members:
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"
    - "dimensionality reduction in deep learning for chest x ray analysis of lung cancer"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "comparative performance analysis of neural networks architectures on h2o platform for various activation functions"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "comparative analysis of open source frameworks for machine learning with use case in single threaded and multi threaded modes"
    - "performance analysis of open source machine learning frameworks for various parameters in single threaded and multi threaded modes"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"

Connected through hyperedge HE-4 (induced by source paper "dimensionality reduction in deep learning for chest x ray analysis of lung cancer"):
  with members:
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "comparative performance analysis of neural networks architectures on h2o platform for various activation functions"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "comparative analysis of open source frameworks for machine learning with use case in single threaded and multi threaded modes"
    - "performance analysis of open source machine learning frameworks for various parameters in single threaded and multi threaded modes"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "when does bone suppression and lung field segmentation improve chest x ray disease classification"):
  with members:
    - "comparison of deep learning approaches for multi label chest x ray classification"
    - "iterative attention mining for weakly supervised thoracic disease pattern localization in chest x rays"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "learning to read chest x rays recurrent neural cascade model for automated image annotation"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

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.LG(Machine Learning)