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: "a neural autoregressive approach to collaborative filtering"
  Abstract: This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive Distribution Estimator (NADE). We first describe the basic CF-NADE model for CF tasks. Then we propose to improve the model by sharing parameters between different ratings. A factored version of CF-NADE is also proposed for better scalability. Furthermore, we take the ordinal nature of the preferences into consideration and propose an ordinal cost to optimize CF-NADE, which shows superior performance. Finally, CF-NADE can be extended to a deep model, with only moderately increased computational complexity. Experimental results show that CF-NADE with a single hidden layer beats all previous state-of-the-art methods on MovieLens 1M, MovieLens 10M, and Netflix datasets, and adding more hidden layers can further improve the performance.

Connected through hyperedge HE-1 (induced by source paper "nat neural architecture transformer for accurate and compact architectures"):
  with members:
    - "multi marginal wasserstein gan"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "the evolved transformer"
    - "learnable embedding space for efficient neural architecture compression"
    - "graph convolutional networks for temporal action localization"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "adversarial learning with local coordinate coding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "why not to use zero imputation correcting sparsity bias in training neural networks"):
  with members:
    - "misgan learning from incomplete data with generative adversarial networks"
    - "gain missing data imputation using generative adversarial nets"
    - "brits bidirectional recurrent imputation for time series"
    - "dropblock a regularization method for convolutional networks"
    - "deep sets"
    - "self normalizing neural networks"
    - "mida multiple imputation using denoising autoencoders"
    - "neural collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "low rank matrix completion a contemporary survey"):
  with members:
    - "generalized sparse and low rank optimization for ultra dense networks"
    - "neural collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "overview of full dimension mimo in lte advanced pro"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "towards amortized ranking critical training for collaborative filtering"):
  with members:
    - "eigenrec generalizing puresvd for effective and efficient top n recommendations"
    - "embarrassingly shallow autoencoders for sparse data"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "deep learning based recommender system a survey and new perspectives"
    - "cgans with projection discriminator"
    - "outer product based neural collaborative filtering"
    - "adversarial personalized ranking for recommendation"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-5 (induced by source paper "inductive matrix completion based on graph neural networks"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "link prediction based on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "how powerful are graph neural networks"
    - "spectral collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "geometric matrix completion with deep conditional random fields"):
  with members:
    - "regularizing autoencoder based matrix completion models via manifold learning"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "deep learning markov random field for semantic segmentation"
    - "robust on line matrix completion on graphs"
    - "semi supervised classification with graph convolutional networks"
    - "hybrid recommender system based on autoencoders"
    - "learning deep structure preserving image text embeddings"

Connected through hyperedge HE-7 (induced by source paper "joint neural collaborative filtering for recommender systems"):
  with members:
    - "neural graph collaborative filtering"
    - "deep learning based recommender system a survey and new perspectives"
    - "attention based hierarchical neural query suggestion"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "joint deep modeling of users and items using reviews for recommendation"

Connected through hyperedge HE-8 (induced by source paper "jscn joint spectral convolutional network for cross domain recommendation"):
  with members:
    - "deep cross networks with aesthetic preference for cross domain recommendation"
    - "deep learning based recommender system a survey and new perspectives"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "conet collaborative cross networks for cross domain recommendation"
    - "spectral collaborative filtering"
    - "neural collaborative filtering"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "cross stitch networks for multi task learning"

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.IR(Information Retrieval)