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: "deepheart semi supervised sequence learning for cardiovascular risk prediction"
  Abstract: We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep apnea (0.8298). We compare two semi-supervised train- ing methods, semi-supervised sequence learning and heuristic pretraining, and show they outperform hand-engineered biomarkers from the medical literature. We believe our work suggests a new approach to patient risk stratification based on cardiovascular risk scores derived from popular wearables such as Fitbit, Apple Watch, or Android Wear.

Connected through hyperedge HE-1 (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"
    - "automatic calcium scoring in low dose chest ct using deep neural networks with dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "hardware guided symbiotic training for compact accurate yet execution efficient lstm"):
  with members:
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "an analysis of neural language modeling at multiple scales"
    - "chamnet towards efficient network design through platform aware model adaptation"
    - "grow and prune compact fast and accurate lstms"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "block sparse recurrent neural networks"
    - "mnasnet platform aware neural architecture search for mobile"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-3 (induced by source paper "diabdeep pervasive diabetes diagnosis based on wearable medical sensors and efficient neural networks"):
  with members:
    - "incremental learning using a grow and prune paradigm with efficient neural networks"
    - "scann synthesis of compact and accurate neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "learning deep representations with probabilistic knowledge transfer"
    - "a microprocessor implemented in 65nm cmos with configurable and bit scalable accelerator for programmable in memory computing"
    - "verideep verifying integrity of deep neural networks through sensitive sample fingerprinting"
    - "grow and prune compact fast and accurate lstms"
    - "shift a zero flop zero parameter alternative to spatial convolutions"

Connected through hyperedge HE-4 (induced by source paper "learning individualized cardiovascular responses from large scale wearable sensors data"):
  with members:
    - "attention is all you need"
    - "harnessing the web for population scale physiological sensing a case study of sleep and performance"
    - "xgboost a scalable tree boosting system"
    - "wavenet a generative model for raw audio"
    - "adam a method for stochastic optimization"

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.AI(Artificial Intelligence)