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: "neural feature learning from relational database"
  Abstract: Feature engineering is one of the most important but most tedious tasks in data science. This work studies automation of feature learning from relational database. We first prove theoretically that finding the optimal features from relational data for predictive tasks is NP-hard. We propose an efficient rule-based approach based on heuristics and a deep neural network to automatically learn appropriate features from relational data. We benchmark our approaches in ensembles in past Kaggle competitions. Our new approach wins late medals and beats the state-of-the-art solutions with significant margins. To the best of our knowledge, this is the first time an automated data science system could win medals in Kaggle competitions with complex relational database.

Connected through hyperedge HE-1 (induced by source paper "supervised learning on relational databases with graph neural networks"):
  with members:
    - "on graph classification networks datasets and baselines"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "a comprehensive survey on graph neural networks"
    - "tabnet attentive interpretable tabular learning"
    - "relational pooling for graph representations"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-2 (induced by source paper "deep models for relational databases"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "on the universality of invariant networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "adaptive sampling towards fast graph representation learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "invariant and equivariant graph networks"

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"
    - "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"
    - "learning transferable architectures for scalable image recognition"

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)