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: "artificial neural networks applied to taxi destination prediction"
  Abstract: We describe our first-place solution to the ECML/PKDD discovery challenge on taxi destination prediction. The task consisted in predicting the destination of a taxi based on the beginning of its trajectory, represented as a variable-length sequence of GPS points, and diverse associated meta-information, such as the departure time, the driver id and client information. Contrary to most published competitor approaches, we used an almost fully automated approach based on neural networks and we ranked first out of 381 teams. The architectures we tried use multi-layer perceptrons, bidirectional recurrent neural networks and models inspired from recently introduced memory networks. Our approach could easily be adapted to other applications in which the goal is to predict a fixed-length output from a variable-length sequence.

Connected through hyperedge HE-1 (induced by source paper "fastai a layered api for deep learning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "developing an app to interpret chest x rays to support the diagnosis of respiratory pathology with artificial intelligence"
    - "suggestion mining from online reviews using ulmfit"
    - "multiclass language identification using deep learning on spectral images of audio signals"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "mish a self regularized non monotonic neural activation function"
    - "bag of tricks for image classification with convolutional neural networks"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-2 (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"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "interleaved sequence rnns for fraud detection"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "deep learning in neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "a survey of techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "embedding visual hierarchy with deep networks for large scale visual recognition"

Connected through hyperedge HE-5 (induced by source paper "techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "autoaugment learning augmentation policies from data"
    - "deep forest"

Connected through hyperedge HE-6 (induced by source paper "improving missing data imputation with deep generative models"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "handling incomplete heterogeneous data using vaes"
    - "large scale gan training for high fidelity natural image synthesis"
    - "airline passenger name record generation using generative adversarial networks"
    - "synthesizing tabular data using generative adversarial networks"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "mida multiple imputation using denoising autoencoders"
    - "generating multi label discrete patient records using generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "mpe a mobility pattern embedding model for predicting next locations"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "gt seer geo temporal sequential embedding rank for point of interest recommendation"
    - "distributed representations of sentences and documents"
    - "nlpmm a next location predictor with markov modeling"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "applications of the bounded total variation denoising method to urban traffic analysis"):
  with members:
    - "error bounds for approximations with deep relu networks"
    - "why deep neural networks for function approximation"
    - "exact matrix completion via convex optimization"

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)