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 "deep learning in cardiology"):
  Abstract: The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction, and intervention. Deep learning is a representation learning method that consists of layers that transform data nonlinearly, thus, revealing hierarchical relationships and structures. In this review, we survey deep learning application papers that use structured data, and signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.

Includes members:
  Member "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ophthalmic diagnosis using deep learning with fundus images a critical review")
  Member "estimation of the volume of the left ventricle from mri images using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d roi aware u net for accurate and efficient colorectal tumor segmentation")
  Member "deep learning analysis of the myocardium in coronary ct angiography for identification of patients with functionally significant coronary artery stenosis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparsely activated networks a new method for decomposing and compressing data")
  Member "convolutional capsule network for classification of breast cancer histology images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "examining the benefits of capsule neural networks")
  Member "predicting cardiovascular risk factors in retinal fundus photographs using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs")
  Member "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "brain tumor type classification via capsule networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deepheart semi supervised sequence learning for cardiovascular risk prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hardware guided symbiotic training for compact accurate yet execution efficient lstm")

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)