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: "multi task prediction of disease onsets from longitudinal lab tests"
  Abstract: Disparate areas of machine learning have benefited from models that can take raw data with little preprocessing as input and learn rich representations of that raw data in order to perform well on a given prediction task. We evaluate this approach in healthcare by using longitudinal measurements of lab tests, one of the more raw signals of a patient's health state widely available in clinical data, to predict disease onsets. In particular, we train a Long Short-Term Memory (LSTM) recurrent neural network and two novel convolutional neural networks for multi-task prediction of disease onset for 133 conditions based on 18 common lab tests measured over time in a cohort of 298K patients derived from 8 years of administrative claims data. We compare the neural networks to a logistic regression with several hand-engineered, clinically relevant features. We find that the representation-based learning approaches significantly outperform this baseline. We believe that our work suggests a new avenue for patient risk stratification based solely on lab results.

Connected through hyperedge HE-1 (induced by source paper "a deep learning approach to diagnosing multiple sclerosis from smartphone data"):
  with members:
    - "cxplain causal explanations for model interpretation under uncertainty"
    - "not to cry wolf distantly supervised multitask learning in critical care"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "granger causal attentive mixtures of experts learning important features with neural networks"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "attention is all you need"
    - "a unified approach to interpreting model predictions"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"

Connected through hyperedge HE-2 (induced by source paper "multi task self supervised learning for human activity detection"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "deep learning for sensor based activity recognition a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "universal language model fine tuning for text classification"
    - "protecting sensory data against sensitive inferences"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-3 (induced by source paper "machine learning for early prediction of circulatory failure in the intensive care unit"):
  with members:
    - "deepsofa a continuous acuity score for critically ill patients using clinically interpretable deep learning"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "scalable and accurate deep learning for electronic health records"
    - "a unified approach to interpreting model predictions"
    - "improving palliative care with deep learning"
    - "predicting severe sepsis using text from the electronic health record"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"

Connected through hyperedge HE-4 (induced by source paper "learning tasks for multitask learning heterogenous patient populations in the icu"):
  with members:
    - "comparing rule based and deep learning models for patient phenotyping"
    - "the use of autoencoders for discovering patient phenotypes"
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "personalized risk scoring for critical care patients using mixtures of gaussian process experts"
    - "recurrent neural networks for multivariate time series with missing values"
    - "show attend and tell neural image caption generation with visual attention"
    - "doctor ai predicting clinical events via recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "scalable and accurate deep learning for electronic health records"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "clinical intervention prediction and understanding using deep networks"
    - "improving palliative care with deep learning"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "recurrent neural networks for multivariate time series with missing values"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "semi supervised sequence learning"
    - "learning to diagnose with lstm recurrent neural networks"

Connected through hyperedge HE-6 (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"
    - "adversarial images for variational autoencoders"
    - "deepqa improving the estimation of single protein model quality with deep belief networks"
    - "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-7 (induced by source paper "improving hospital mortality prediction with medical named entities and multimodal learning"):
  with members:
    - "deep ehr chronic disease prediction using medical notes"
    - "clinical intervention prediction and understanding using deep networks"
    - "deep active learning for named entity recognition"
    - "efficient vector representation for documents through corruption"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "recurrent neural networks for multivariate time series with missing values"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "learning to diagnose with lstm recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "deepheart semi supervised sequence learning for cardiovascular risk prediction"):
  with members:
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "identity mappings in deep residual networks"
    - "show attend and tell neural image caption generation with visual attention"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "learning to diagnose with lstm recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "a clockwork rnn"

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)