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: "predicting clinical events by combining static and dynamic information using recurrent neural networks"
  Abstract: In clinical data sets we often find static information (e.g. gender of the patients, blood type, etc.) combined with sequences of data that are recorded during multiple hospital visits (e.g. medications prescribed, tests performed, etc.). Recurrent Neural Networks (RNNs) have proven to be very successful for modelling sequences of data in many areas of Machine Learning. In this work we present an approach based on RNNs that is specifically designed for the clinical domain and that combines static and dynamic information in order to predict future events. We work with a database collected in the Charit\'{e} Hospital in Berlin that contains all the information concerning patients that underwent a kidney transplantation. After the transplantation three main endpoints can occur: rejection of the kidney, loss of the kidney and death of the patient. Our goal is to predict, given the Electronic Health Record of each patient, whether any of those endpoints will occur within the next six or twelve months after each visit to the clinic. We compared different types of RNNs that we developed for this work, a model based on a Feedforward Neural Network and a Logistic Regression model. We found that the RNN that we developed based on Gated Recurrent Units provides the best performance for this task. We also performed an additional experiment using these models to predict next actions and found that for such use case the model based on a Feedforward Neural Network outperformed the other models. Our hypothesis is that long-term dependencies are not as relevant in this task.

Connected through hyperedge HE-1 (induced by source paper "raim recurrent attentive and intensive model of multimodal patient monitoring data"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "attention is all you need"
    - "highrisk prediction from electronic medical records via deep attention networks"
    - "deep learning to attend to risk in icu"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "temporal relational reasoning in videos"

Connected through hyperedge HE-2 (induced by source paper "disease phenotyping using deep learning a diabetes case study"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "scalable and accurate deep learning for electronic health records"
    - "finding algebraic structure of care in time a deep learning approach"
    - "improving palliative care with deep learning"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "learning to diagnose with lstm recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "modeling the intensity function of point process via recurrent neural networks"):
  with members:
    - "recurrent neural networks for multivariate time series with missing values"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "draw a recurrent neural network for image generation"
    - "rmsprop and equilibrated adaptive learning rates for non convex optimization"
    - "a holistic approach to log data analysis in high performance computing systems the case of ibm blue gene q"

Connected through hyperedge HE-4 (induced by source paper "retain interpretable predictive model in healthcare using reverse time attention mechanism"):
  with members:
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "a neural attention model for abstractive sentence summarization"
    - "visualizing and understanding recurrent networks"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "draw a recurrent neural network for image generation"
    - "attention based models for speech recognition"
    - "learning to diagnose with lstm recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "predictive clinical decision support system with rnn encoding and tensor decoding"):
  with members:
    - "going digital a survey on digitalization and large scale data analytics in healthcare"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "theano new features and speed improvements"

Connected through hyperedge HE-6 (induced by source paper "going digital a survey on digitalization and large scale data analytics in healthcare"):
  with members:
    - "privacy and data protection by design from policy to engineering"
    - "towards a new science of a clinical data intelligence"

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)