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: "hybrid recommender system based on autoencoders"
  Abstract: A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings. In the last decades, few attempts where done to handle that objective with Neural Networks, but recently an architecture based on Autoencoders proved to be a promising approach. In current paper, we enhanced that architecture (i) by using a loss function adapted to input data with missing values, and (ii) by incorporating side information. The experiments demonstrate that while side information only slightly improve the test error averaged on all users/items, it has more impact on cold users/items.

Connected through hyperedge HE-1 (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"
    - "a neural autoregressive approach to collaborative filtering"
    - "learning deep structure preserving image text embeddings"

Connected through hyperedge HE-2 (induced by source paper "from personalization to privatization meta matrix factorization for private rating predictions"):
  with members:
    - "neural variational hybrid collaborative filtering"
    - "on first order meta learning algorithms"
    - "outer product based neural collaborative filtering"
    - "a collective variational autoencoder for top n recommendation with side information"
    - "federated meta learning for recommendation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "federated meta learning with fast convergence and efficient communication"
    - "neural collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "a collaborative filtering model with heterogeneous neural networks for recommender systems"):
  with members:
    - "collaborative filtering with topic and social latent factors incorporating implicit feedback"
    - "recurrent poisson factorization for temporal recommendation"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "melu meta learned user preference estimator for cold start recommendation"):
  with members:
    - "memory matching networks for one shot image recognition"
    - "neural collaborative filtering"
    - "prototypical networks for few shot learning"
    - "meta sgd learning to learn quickly for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "wide deep learning for recommender systems"
    - "item2vec neural item embedding for collaborative filtering"
    - "matching networks for one shot learning"

Connected through hyperedge HE-5 (induced by source paper "a general graph based framework for top n recommendation using content temporal and trust information"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "trust aware top n recommender systems with correlative denoising autoencoder"
    - "pixie a system for recommending 3 billion items to 200 million users in real time"
    - "content based top n recommendation using heterogeneous relations"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "content based book recommending using learning for text categorization"

Connected through hyperedge HE-6 (induced by source paper "on hybrid modular recommendation systems for video streaming"):
  with members:
    - "neural autoregressive collaborative filtering for implicit feedback"
    - "matrix completion with queries"
    - "feature weighted linear stacking"

Connected through hyperedge HE-7 (induced by source paper "adversarial collaborative auto encoder for top n recommendation"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "adversarial personalized ranking for recommendation"
    - "a collective variational autoencoder for top n recommendation with side information"
    - "neural collaborative filtering"
    - "trust aware top n recommender systems with correlative denoising autoencoder"
    - "collaborative autoencoder for recommender systems"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"

Connected through hyperedge HE-8 (induced by source paper "regularizing autoencoder based matrix completion models via manifold learning"):
  with members:
    - "twitter user geolocation using deep multiview learning"
    - "deep learning sparse ternary projections for compressed sensing of images"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "hybrid collaborative filtering with neural networks"
    - "hybrid collaborative filtering with autoencoders"
    - "a neural autoregressive approach to collaborative filtering"
    - "deep residual learning for 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.LG(Machine Learning)