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: "cardiologist level arrhythmia detection with convolutional neural networks"
  Abstract: We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor. We build a dataset with more than 500 times the number of unique patients than previously studied corpora. On this dataset, we train a 34-layer convolutional neural network which maps a sequence of ECG samples to a sequence of rhythm classes. Committees of board-certified cardiologists annotate a gold standard test set on which we compare the performance of our model to that of 6 other individual cardiologists. We exceed the average cardiologist performance in both recall (sensitivity) and precision (positive predictive value).

Connected through hyperedge HE-1 (induced by source paper "model assertions for monitoring and improving ml model"):
  with members:
    - "better ai through logical scaffolding"
    - "continuous integration of machine learning models with ease ml ci towards a rigorous yet practical treatment"
    - "outside the box abstraction based monitoring of neural networks"
    - "scaling video analytics on constrained edge nodes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "vstore a data store for analytics on large videos"
    - "selection via proxy efficient data selection for deep learning"
    - "formal security analysis of neural networks using symbolic intervals"

Connected through hyperedge HE-2 (induced by source paper "collaborative inference for efficient remote monitoring"):
  with members:
    - "deep learning for time series classification a review"
    - "efficient inference on deep neural networks by dynamic representations and decision gates"
    - "deep feature compression for collaborative object detection"
    - "doubly nested network for resource efficient inference"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "xgboost a scalable tree boosting system"
    - "doubly convolutional neural networks"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "rethinking 1d cnn for time series classification a stronger baseline"):
  with members:
    - "convtimenet a pre trained deep convolutional neural network for time series classification"
    - "inceptiontime finding alexnet for time series classification"
    - "towards a universal neural network encoder for time series"
    - "deep learning for time series analysis"
    - "multi scale convolutional neural networks for time series classification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "should we really use post hoc tests based on mean ranks"

Connected through hyperedge HE-4 (induced by source paper "deep learning in cardiology"):
  with members:
    - "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"
    - "estimation of the volume of the left ventricle from mri images using deep neural networks"
    - "deep learning analysis of the myocardium in coronary ct angiography for identification of patients with functionally significant coronary artery stenosis"
    - "convolutional capsule network for classification of breast cancer histology images"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "brain tumor type classification via capsule networks"
    - "deepheart semi supervised sequence learning for cardiovascular risk prediction"

Connected through hyperedge HE-5 (induced by source paper "detecting and interpreting myocardial infarction using fully convolutional neural networks"):
  with members:
    - "deep neural network ensembles for time series classification"
    - "multivariate lstm fcns for time series classification"
    - "tsviz demystification of deep learning models for time series analysis"
    - "ecg heartbeat classification a deep transferable representation"
    - "lstm fully convolutional networks for time series classification"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "axiomatic attribution for deep networks"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "meta learning for few shot time series classification"):
  with members:
    - "convtimenet a pre trained deep convolutional neural network for time series classification"
    - "unsupervised scalable representation learning for multivariate time series"
    - "deep learning for time series classification a review"
    - "a cnn adapted to time series for the classification of supernovae"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "autowarp learning a warping distance from unlabeled time series using sequence autoencoders"
    - "transfer learning for time series classification"

Connected through hyperedge HE-7 (induced by source paper "unsuperpoint end to end unsupervised interest point detector and descriptor"):
  with members:
    - "lf net learning local features from images"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "geometric loss functions for camera pose regression with deep learning"
    - "superpoint self supervised interest point detection and description"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling"):
  with members:
    - "learning a physical long term predictor"
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "arrhythmia classification from the abductive interpretation of short single lead ecg records"
    - "convolutional recurrent neural networks for electrocardiogram classification"
    - "on loss functions for deep neural networks in classification"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

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)