human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "gru ode bayes continuous modeling of sporadically observed time series"):
  Abstract: Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i.e., sampling is irregular both in time and across dimensions)-such as in the case of clinical patient data. To address these challenges, we propose (1) a continuous-time version of the Gated Recurrent Unit, building upon the recent Neural Ordinary Differential Equations (Chen et al., 2018), and (2) a Bayesian update network that processes the sporadic observations. We bring these two ideas together in our GRU-ODE-Bayes method. We then demonstrate that the proposed method encodes a continuity prior for the latent process and that it can exactly represent the Fokker-Planck dynamics of complex processes driven by a multidimensional stochastic differential equation. Additionally, empirical evaluation shows that our method outperforms the state of the art on both synthetic data and real-world data with applications in healthcare and climate forecast. What is more, the continuity prior is shown to be well suited for low number of samples settings.

Includes members:
  Member "early recognition of sepsis with gaussian process temporal convolutional networks and dynamic time warping":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explainable artificial intelligence model to predict acute critical illness from electronic health records")
  Member "latent odes for irregularly sampled time series":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network")
  Member "deep ensemble tensor factorization for longitudinal patient trajectories classification":
    also connected through other hyperedges:
      (none)
  Member "stable architectures for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks")
  Member "neural ordinary differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic latent residual video prediction")
  Member "scalable and accurate deep learning for electronic health records":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "zimm a deep learning model for long term and blurry relapses with non clinical claims data")
  Member "the neural hawkes process a neurally self modulating multivariate point process":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "d ej a vu a contextualized temporal attention mechanism for sequential recommendation")
  Member "directly modeling missing data in sequences with rnns improved classification of clinical time series":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deep learning approach to diagnosing multiple sclerosis from smartphone data")

Question: Please tell me which class the source paper 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)