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: "entity embeddings of categorical variables"
  Abstract: We map categorical variables in a function approximation problem into Euclidean spaces, which are the entity embeddings of the categorical variables. The mapping is learned by a neural network during the standard supervised training process. Entity embedding not only reduces memory usage and speeds up neural networks compared with one-hot encoding, but more importantly by mapping similar values close to each other in the embedding space it reveals the intrinsic properties of the categorical variables. We applied it successfully in a recent Kaggle competition and were able to reach the third position with relative simple features. We further demonstrate in this paper that entity embedding helps the neural network to generalize better when the data is sparse and statistics is unknown. Thus it is especially useful for datasets with lots of high cardinality features, where other methods tend to overfit. We also demonstrate that the embeddings obtained from the trained neural network boost the performance of all tested machine learning methods considerably when used as the input features instead. As entity embedding defines a distance measure for categorical variables it can be used for visualizing categorical data and for data clustering.

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 "eigen stratified models"):
  with members:
    - "a distributed method for fitting laplacian regularized stratified models"
    - "approximating the spectrum of a graph"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "supertml two dimensional word embedding for the precognition on structured tabular data"):
  with members:
    - "squared english word a method of generating glyph to use super characters for sentiment analysis"
    - "neural feature learning from relational database"
    - "deep contextualized word representations"
    - "ultra power efficient cnn domain specific accelerator with 9 3tops watt for mobile and embedded applications"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "super characters a conversion from sentiment classification to image classification"
    - "catboost unbiased boosting with categorical features"
    - "progressive neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "bootstrapping conditional gans for video game level generation"):
  with members:
    - "general video game rule generation"
    - "doom level generation using generative adversarial networks"
    - "image transformer"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "atdelfi automatically designing legible full instructions for games"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "on convergence and stability of gans"

Connected through hyperedge HE-5 (induced by source paper "λopt learn to regularize recommender models in finer levels"):
  with members:
    - "warm up cold start advertisements improving ctr predictions via learning to learn id embeddings"
    - "adversarial personalized ranking for recommendation"
    - "aesthetic based clothing recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "a generic coordinate descent framework for learning from implicit feedback"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "deep joint embeddings of context and content for recommendation"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "the importance of context when recommending tv content dataset and algorithms"
    - "on the dimensionality of word embedding"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "wide deep learning for recommender systems"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "e commerce in your inbox product recommendations at scale"

Connected through hyperedge HE-7 (induced by source paper "a case study on using deep learning for network intrusion detection"):
  with members:
    - "metrics towards measuring cyber agility"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "a novel hierarchical intrusion detection system based on decision tree and rules based models"
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "predicting cyber attack rates with extreme values"
    - "preventive and reactive cyber defense dynamics is globally stable"
    - "spatiotemporal patterns and predictability of cyberattacks"
    - "characterizing the power of moving target defense via cyber epidemic dynamics"

Connected through hyperedge HE-8 (induced by source paper "network intrusion detection based on lstm and feature embedding"):
  with members:
    - "performance comparison of intrusion detection systems and application of machine learning to snort system"
    - "persistent hidden states and nonlinear transformation for long short term memory"
    - "lstm a search space odyssey"
    - "context dependent word representation for neural machine translation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"

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)