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: "personalized risk scoring for critical care prognosis using mixtures of gaussian processes"
  Abstract: Objective: In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs; the proposed risk scoring system ensures timely intensive care unit (ICU) admissions for clinically deteriorating patients. Methods: The risk scoring system learns a set of latent patient subtypes from the offline electronic health record data, and trains a mixture of Gaussian Process (GP) experts, where each expert models the physiological data streams associated with a specific patient subtype. Transfer learning techniques are used to learn the relationship between a patient's latent subtype and her static admission information (e.g. age, gender, transfer status, ICD-9 codes, etc). Results: Experiments conducted on data from a heterogeneous cohort of 6,321 patients admitted to Ronald Reagan UCLA medical center show that our risk score significantly and consistently outperforms the currently deployed risk scores, such as the Rothman index, MEWS, APACHE and SOFA scores, in terms of timeliness, true positive rate (TPR), and positive predictive value (PPV). Conclusion: Our results reflect the importance of adopting the concepts of personalized medicine in critical care settings; significant accuracy and timeliness gains can be achieved by accounting for the patients' heterogeneity. Significance: The proposed risk scoring methodology can confer huge clinical and social benefits on more than 200,000 critically ill inpatient who exhibit cardiac arrests in the US every year.

Connected through hyperedge HE-1 (induced by source paper "multi task time series analysis applied to drug response modelling"):
  with members:
    - "a survey on multi task learning"
    - "input output non linear dynamical systems applied to physiological condition monitoring"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "radialgan leveraging multiple datasets to improve target specific predictive models using generative adversarial networks"):
  with members:
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "adversarial feature learning"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-3 (induced by source paper "product kernel interpolation for scalable gaussian processes"):
  with members:
    - "a bayesian nonparametric approach for estimating individualized treatment response curves"
    - "improved stochastic trace estimation using mutually unbiased bases"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "gaussian processes for big data"

Connected through hyperedge HE-4 (induced by source paper "gain missing data imputation using generative adversarial nets"):
  with members:
    - "mida multiple imputation using denoising autoencoders"

Connected through hyperedge HE-5 (induced by source paper "estimating missing data in temporal data streams using multi directional recurrent neural networks"):
  with members:
    - "mida multiple imputation using denoising autoencoders"
    - "learning from clinical judgments semi markov modulated marked hawkes processes for risk prognosis"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "xgboost a scalable tree boosting system"
    - "recurrent neural networks for multivariate time series with missing values"
    - "doctor ai predicting clinical events via recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "a hidden absorbing semi markov model for informatively censored temporal data learning and inference"):
  with members:
    - "learning from clinical judgments semi markov modulated marked hawkes processes for risk prognosis"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "input output non linear dynamical systems applied to physiological condition monitoring"
    - "balancing suspense and surprise timely decision making with endogenous information acquisition"
    - "recurrent neural networks for multivariate time series with missing values"
    - "expectation maximization and complex duration distributions for continuous time bayesian networks"

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)