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: "learning from clinical judgments semi markov modulated marked hawkes processes for risk prognosis"
  Abstract: Critically ill patients in regular wards are vulnerable to unanticipated adverse events which require prompt transfer to the intensive care unit (ICU). To allow for accurate prognosis of deteriorating patients, we develop a novel continuous-time probabilistic model for a monitored patient's temporal sequence of physiological data. Our model captures "informatively sampled" patient episodes: the clinicians' decisions on when to observe a hospitalized patient's vital signs and lab tests over time are represented by a marked Hawkes process, with intensity parameters that are modulated by the patient's latent clinical states, and with observable physiological data (mark process) modeled as a switching multi-task Gaussian process. In addition, our model captures "informatively censored" patient episodes by representing the patient's latent clinical states as an absorbing semi-Markov jump process. The model parameters are learned from offline patient episodes in the electronic health records via an EM-based algorithm. Experiments conducted on a cohort of patients admitted to a major medical center over a 3-year period show that risk prognosis based on our model significantly outperforms the currently deployed medical risk scores and other baseline machine learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "learning multivariate hawkes processes at scale"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "latent odes for irregularly sampled time series"
    - "community interaction and conflict on the web"
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "fake news mitigation via point process based intervention"
    - "wasserstein learning of deep generative point process models"
    - "learning granger causality for hawkes processes"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-2 (induced by source paper "temporal clustering invariance in irregular healthcare time series"):
  with members:
    - "on the convergence of adam and beyond"
    - "interpolation prediction networks for irregularly sampled time series"
    - "learning to exploit invariances in clinical time series data using sequence transformer networks"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "modeling missing data in clinical time series with rnns"
    - "group equivariant convolutional networks"
    - "recurrent neural networks for multivariate time series with missing values"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-3 (induced by source paper "learning the joint representation of heterogeneous temporal events for clinical endpoint prediction"):
  with members:
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "a clockwork rnn"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (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"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "xgboost a scalable tree boosting system"
    - "personalized risk scoring for critical care prognosis using mixtures of gaussian processes"
    - "recurrent neural networks for multivariate time series with missing values"
    - "doctor ai predicting clinical events via recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "a hidden absorbing semi markov model for informatively censored temporal data learning and inference"):
  with members:
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "personalized risk scoring for critical care prognosis using mixtures of gaussian processes"
    - "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.LG(Machine Learning)