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 "automated and interpretable patient ecg profiles for disease detection tracking and discovery"):
  Abstract: The electrocardiogram or ECG has been in use for over 100 years and remains the most widely performed diagnostic test to characterize cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, innovations in machine learning algorithms, and availability of large-scale digitized ECG data would enable extending the utility of the ECG beyond its current limitations, while at the same time preserving interpretability, which is fundamental to medical decision-making. We identified 36,186 ECGs from the UCSF database that were 1) in normal sinus rhythm and 2) would enable training of specific models for estimation of cardiac structure or function or detection of disease. We derived a novel model for ECG segmentation using convolutional neural networks (CNN) and Hidden Markov Models (HMM) and evaluated its output by comparing electrical interval estimates to 141,864 measurements from the clinical workflow. We built a 725-element patient-level ECG profile using downsampled segmentation data and trained machine learning models to estimate left ventricular mass, left atrial volume, mitral annulus e' and to detect and track four diseases: pulmonary arterial hypertension (PAH), hypertrophic cardiomyopathy (HCM), cardiac amyloid (CA), and mitral valve prolapse (MVP). CNN-HMM derived ECG segmentation agreed with clinical estimates, with median absolute deviations (MAD) as a fraction of observed value of 0.6% for heart rate and 4% for QT interval. Patient-level ECG profiles enabled quantitative estimates of left ventricular and mitral annulus e' velocity with good discrimination in binary classification models of left ventricular hypertrophy and diastolic function. Models for disease detection ranged from AUROC of 0.94 to 0.77 for MVP. Top-ranked variables for all models included known ECG characteristics along with novel predictors of these traits/diseases.

Includes members:
  Member "high resolution breast cancer screening with multi view deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "cardiologist level arrhythmia detection with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "model assertions for monitoring and improving ml model")
  Member "a web deployed computer vision pipeline for automated determination of cardiac structure and function and detection of disease by two dimensional echocardiography":
    also connected through other hyperedges:
      (none)
  Member "the mythos of model interpretability":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "tensorflow large scale machine learning on heterogeneous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "scikit image image processing in python":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "surveiledge real time video query based on collaborative cloud edge deep learning")

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)