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: "deepar probabilistic forecasting with autoregressive recurrent networks"
  Abstract: Probabilistic forecasting, i.e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes. In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place. In this paper we propose DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an auto regressive recurrent network model on a large number of related time series. We demonstrate how by applying deep learning techniques to forecasting, one can overcome many of the challenges faced by widely-used classical approaches to the problem. We show through extensive empirical evaluation on several real-world forecasting data sets accuracy improvements of around 15% compared to state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "multi variate probabilistic time series forecasting via conditioned normalizing flows"):
  with members:
    - "high dimensional multivariate forecasting with low rank gaussian copula processes"
    - "videoflow a flow based generative model for video"
    - "n beats neural basis expansion analysis for interpretable time series forecasting"
    - "enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting"
    - "conditional recurrent flow conditional generation of longitudinal samples with applications to neuroimaging"
    - "relational inductive biases deep learning and graph networks"
    - "attention is all you need"
    - "modeling long and short term temporal patterns with deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "meta learning framework with applications to zero shot time series forecasting"):
  with members:
    - "energy predictive models with limited data using transfer learning"
    - "decentralized flood forecasting using deep neural networks"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "financial time series forecasting with deep learning a systematic literature review 2005 2019"
    - "n beats neural basis expansion analysis for interpretable time series forecasting"
    - "meta learning with latent embedding optimization"
    - "transfer learning for time series classification"
    - "tadam task dependent adaptive metric for improved few shot learning"

Connected through hyperedge HE-3 (induced by source paper "block hankel tensor arima for multiple short time series forecasting"):
  with members:
    - "manifold modeling in embedded space a perspective for interpreting deep image prior"
    - "model agnostic time series analysis via matrix estimation"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "long term forecasting using higher order tensor rnns"
    - "xgboost a scalable tree boosting system"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"

Connected through hyperedge HE-4 (induced by source paper "customizing sequence generation with multi task dynamical systems"):
  with members:
    - "multi task time series analysis applied to drug response modelling"
    - "quaternet a quaternion based recurrent model for human motion"
    - "disentangled sequential autoencoder"
    - "learning to decompose and disentangle representations for video prediction"
    - "stable recurrent models"
    - "decomposing motion and content for natural video sequence prediction"
    - "filtering variational objectives"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-5 (induced by source paper "recurrent neural networks for time series forecasting current status and future directions"):
  with members:
    - "n beats neural basis expansion analysis for interpretable time series forecasting"
    - "sales demand forecast in e commerce using a long short term memory neural network methodology"
    - "gluonts probabilistic time series models in python"
    - "temporal pattern attention for multivariate time series forecasting"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "forecasting across time series databases using recurrent neural networks on groups of similar series a clustering approach"

Connected through hyperedge HE-6 (induced by source paper "enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting"):
  with members:
    - "generating long sequences with sparse transformers"
    - "generating wikipedia by summarizing long sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "attention is all you need"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "condensenet an efficient densenet using learned group convolutions"
    - "long term forecasting using higher order tensor rnns"

Connected through hyperedge HE-7 (induced by source paper "re examination of the role of latent variables in sequence modeling"):
  with members:
    - "stcn stochastic temporal convolutional networks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "stochastic wavenet a generative latent variable model for sequential data"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "pixelsnail an improved autoregressive generative model"
    - "sgdr stochastic gradient descent with restarts"
    - "neural autoregressive distribution estimation"
    - "density estimation using real nvp"

Connected through hyperedge HE-8 (induced by source paper "you may not need order in time series forecasting"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "the evolved transformer"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "self attention with relative position representations"
    - "deep contextualized word representations"

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.AI(Artificial Intelligence)