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: "autoregressive convolutional neural networks for asynchronous time series"
  Abstract: We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series. The model is inspired by standard autoregressive (AR) models and gating mechanisms used in recurrent neural networks. It involves an AR-like weighting system, where the final predictor is obtained as a weighted sum of adjusted regressors, while the weights are datadependent functions learnt through a convolutional network. The architecture was designed for applications on asynchronous time series and is evaluated on such datasets: a hedge fund proprietary dataset of over 2 million quotes for a credit derivative index, an artificially generated noisy autoregressive series and UCI household electricity consumption dataset. The proposed architecture achieves promising results as compared to convolutional and recurrent neural networks.

Connected through hyperedge HE-1 (induced by source paper "forecastnet a time variant deep feed forward neural network architecture for multi step ahead time series forecasting"):
  with members:
    - "deep transformer models for time series forecasting the influenza prevalence case"
    - "recurrent neural networks for time series forecasting current status and future directions"
    - "enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting"
    - "foundations of sequence to sequence modeling for time series"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "wavenet a generative model for raw audio"
    - "deep learning in neural networks"

Connected through hyperedge HE-2 (induced by source paper "band limited training and inference for convolutional neural networks"):
  with members:
    - "training behavior of deep neural network in frequency domain"
    - "amc automl for model compression and acceleration on mobile devices"
    - "high accuracy low precision training"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "training deep neural networks with 8 bit floating point numbers"
    - "efficient sparse winograd convolutional neural networks"
    - "fft convolutions are faster than winograd on modern cpus here is why"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-3 (induced by source paper "modeling combinatorial evolution in time series prediction"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "graph networks as learnable physics engines for inference and control"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "relational inductive bias for physical construction in humans and machines"
    - "action schema networks generalised policies with deep learning"
    - "toeplitz inverse covariance based clustering of multivariate time series data"

Connected through hyperedge HE-4 (induced by source paper "capturing evolution genes for time series data"):
  with members:
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "modeling interdependent and periodic real world action sequences"
    - "which training methods for gans do actually converge"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "cvae gan fine grained image generation through asymmetric training"

Connected through hyperedge HE-5 (induced by source paper "towards better forecasting by fusing near and distant future visions"):
  with members:
    - "correlated time series forecasting using deep neural networks a summary of results"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "towards dropout training for convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "data driven vehicle trajectory forecasting"):
  with members:
    - "an lstm network for highway trajectory prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (induced by source paper "interpolation prediction networks for irregularly sampled time series"):
  with members:
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "recurrent neural networks for multivariate time series with missing values"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "ensemble kalman inversion a derivative free technique for machine learning tasks"):
  with members:
    - "self normalizing neural networks"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "uncertainty quantification in graph based classification of high dimensional data"
    - "a survey on deep learning in medical image analysis"
    - "instance normalization the missing ingredient for fast stylization"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "training neural networks without gradients a scalable admm approach"

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)