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: "interpretation of mammogram and chest radiograph reports using deep neural networks preliminary results"
  Abstract: Radiology reports are an important means of communication between radiologists and other physicians. These reports express a radiologist's interpretation of a medical imaging examination and are critical in establishing a diagnosis and formulating a treatment plan. In this paper, we propose a Bi-directional convolutional neural network (Bi-CNN) model for the interpretation and classification of mammograms based on breast density and chest radiographic radiology reports based on the basis of chest pathology. The proposed approach helps to organize databases of radiology reports, retrieve them expeditiously, and evaluate the radiology report that could be used in an auditing system to decrease incorrect diagnoses. Our study revealed that the proposed Bi-CNN outperforms the random forest and the support vector machine methods.

Connected through hyperedge HE-1 (induced by source paper "justifying diagnosis decisions by deep neural networks"):
  with members:
    - "on the automatic generation of medical imaging reports"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "generating continuous representations of medical texts"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "recent advances in recurrent neural networks"):
  with members:
    - "gated orthogonal recurrent units on learning to forget"
    - "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "recurrent dropout without memory loss"
    - "recurrent memory network for language modeling"
    - "wavenet a generative model for raw audio"
    - "pixel recurrent neural networks"
    - "zoneout regularizing rnns by randomly preserving hidden activations"

Connected through hyperedge HE-3 (induced by source paper "cylindrical transform 3d semantic segmentation of kidneys with limited annotated images"):
  with members:
    - "recent advances in recurrent neural networks"
    - "an analysis of deep neural network models for practical applications"
    - "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"
    - "image augmentation using radial transform for training deep neural networks"
    - "deep recurrent neural networks for sequential phenotype prediction in genomics"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image 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)