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: "deepqa improving the estimation of single protein model quality with deep belief networks"
  Abstract: Protein quality assessment (QA) by ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiment demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at this http URL

Connected through hyperedge HE-1 (induced by source paper "representation learning with autoencoders for electronic health records a comparative study"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "a survey on deep learning in medical image analysis"
    - "boosting deep learning risk prediction with generative adversarial networks for electronic health records"
    - "adversarial images for variational autoencoders"
    - "safs a deep feature selection approach for precision medicine"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "a predictive approach using deep feature learning for electronic medical records a comparative study"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "boosting deep learning risk prediction with generative adversarial networks for electronic health records"
    - "subic a supervised bi clustering approach for precision medicine"
    - "multi task prediction of disease onsets from longitudinal lab tests"
    - "adversarial images for variational autoencoders"
    - "safs a deep feature selection approach for precision medicine"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "deepgonet multi label prediction of go annotation for protein from sequence using cascaded convolutional and recurrent network"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)