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: "latent odes for irregularly sampled time series"
  Abstract: Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs). We generalize RNNs to have continuous-time hidden dynamics defined by ordinary differential equations (ODEs), a model we call ODE-RNNs. Furthermore, we use ODE-RNNs to replace the recognition network of the recently-proposed Latent ODE model. Both ODE-RNNs and Latent ODEs can naturally handle arbitrary time gaps between observations, and can explicitly model the probability of observation times using Poisson processes. We show experimentally that these ODE-based models outperform their RNN-based counterparts on irregularly-sampled data.

Connected through hyperedge HE-1 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network"):
  with members:
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "recent advances in recurrent neural networks"
    - "nais net stable deep networks from non autonomous differential equations"
    - "lstm a search space odyssey"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"

Connected through hyperedge HE-2 (induced by source paper "scalable gradients for stochastic differential equations"):
  with members:
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "neural jump stochastic differential equations"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "deep learning with differential gaussian process flows"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-3 (induced by source paper "learning multivariate hawkes processes at scale"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "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 from clinical judgments semi markov modulated marked hawkes processes for risk prognosis"
    - "learning granger causality for hawkes processes"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-4 (induced by source paper "progressive growing of neural odes"):
  with members:
    - "interpolation prediction networks for irregularly sampled time series"
    - "neural ordinary differential equations"
    - "teacher student curriculum learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "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"
    - "learning to execute"

Connected through hyperedge HE-5 (induced by source paper "graph neural ordinary differential equations"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "graph neural networks exponentially lose expressive power for node classification"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "hamiltonian graph networks with ode integrators"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "fast and deep graph neural networks"
    - "a differentiable programming system to bridge machine learning and scientific computing"

Connected through hyperedge HE-6 (induced by source paper "continuous graph neural networks"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "gmnn graph markov neural networks"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "diffusion improves graph learning"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "discrete and continuous deep residual learning over graphs"
    - "graph neural networks exponentially lose expressive power for node classification"
    - "simplifying graph convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "infocnf an efficient conditional continuous normalizing flow with adaptive solvers"):
  with members:
    - "conditional adversarial generative flow for controllable image synthesis"
    - "hybrid models with deep and invertible features"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"
    - "analyzing inverse problems with invertible neural networks"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "skipnet learning dynamic routing in convolutional networks"
    - "scaling sgd batch size to 32k for imagenet training"

Connected through hyperedge HE-8 (induced by source paper "neural jump stochastic differential equations"):
  with members:
    - "diffeqflux jl a julia library for neural differential equations"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "solving for high dimensional committor functions using artificial neural networks"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "a dirichlet mixture model of hawkes processes for event sequence clustering"

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)