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: "temporal convolutional neural networks for diagnosis from lab tests"
  Abstract: Early diagnosis of treatable diseases is essential for improving healthcare, and many diseases' onsets are predictable from annual lab tests and their temporal trends. We introduce a multi-resolution convolutional neural network for early detection of multiple diseases from irregularly measured sparse lab values. Our novel architecture takes as input both an imputed version of the data and a binary observation matrix. For imputing the temporal sparse observations, we develop a flexible, fast to train method for differentiable multivariate kernel regression. Our experiments on data from 298K individuals over 8 years, 18 common lab measurements, and 171 diseases show that the temporal signatures learned via convolution are significantly more predictive than baselines commonly used for early disease diagnosis.

Connected through hyperedge HE-1 (induced by source paper "gru ode bayes continuous modeling of sporadically observed time series"):
  with members:
    - "early recognition of sepsis with gaussian process temporal convolutional networks and dynamic time warping"
    - "latent odes for irregularly sampled time series"
    - "deep ensemble tensor factorization for longitudinal patient trajectories classification"
    - "stable architectures for deep neural networks"
    - "neural ordinary differential equations"
    - "scalable and accurate deep learning for electronic health records"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"

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"
    - "learning from clinical judgments semi markov modulated marked hawkes processes for risk prognosis"
    - "modeling missing data in clinical time series with rnns"
    - "group equivariant convolutional networks"
    - "recurrent neural networks for multivariate time series with missing values"

Connected through hyperedge HE-3 (induced by source paper "graph neural network on electronic health records for predicting alzheimer s disease"):
  with members:
    - "graph convolutional transformer learning the graphical structure of electronic health records"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "attention is all you need"
    - "semi supervised classification with graph convolutional networks"
    - "gram graph based attention model for healthcare representation learning"
    - "effective approaches to attention based neural machine translation"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-4 (induced by source paper "deep ehr chronic disease prediction using medical notes"):
  with members:
    - "starspace embed all the things"
    - "multi label classification of patient notes a case study on icd code assignment"
    - "multi task prediction of disease onsets from longitudinal lab tests"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "learning to diagnose with lstm recurrent neural networks"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "recurrent neural networks based obesity status prediction using activity data"):
  with members:
    - "understanding hidden memories of recurrent neural networks"
    - "gram graph based attention model for healthcare representation learning"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "visualizing and understanding recurrent networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "a latent topic model for mining heterogenous non randomly missing electronic health records data"):
  with members:
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "learning to diagnose with lstm recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "boosting deep learning risk prediction with generative adversarial networks for electronic health records"):
  with members:
    - "generative adversarial networks as variational training of energy based models"
    - "mmd gan towards deeper understanding of moment matching network"
    - "exploiting convolutional neural network for risk prediction with medical feature embedding"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "generating images with recurrent adversarial networks"
    - "improved techniques for training gans"
    - "coupled generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "dynamic weight alignment for convolutional neural networks"):
  with members:
    - "deformable convolutional networks"
    - "wavenet a generative model for raw audio"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "a convolutional neural network for modelling sentences"

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)