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: "embarrassingly shallow autoencoders for sparse data"
  Abstract: Combining simple elements from the literature, we define a linear model that is geared toward sparse data, in particular implicit feedback data for recommender systems. We show that its training objective has a closed-form solution, and discuss the resulting conceptual insights. Surprisingly, this simple model achieves better ranking accuracy than various state-of-the-art collaborative-filtering approaches, including deep non-linear models, on most of the publicly available data-sets used in our experiments.

Connected through hyperedge HE-1 (induced by source paper "recvae a new variational autoencoder for top n recommendations with implicit feedback"):
  with members:
    - "towards amortized ranking critical training for collaborative filtering"
    - "on the necessity and effectiveness of learning the prior of variational auto encoder"
    - "isolating sources of disentanglement in variational autoencoders"
    - "boosting trust region policy optimization by normalizing flows policy"
    - "searching for activation functions"
    - "vae with a vampprior"
    - "proximal policy optimization algorithms"
    - "density estimation using real nvp"

Connected through hyperedge HE-2 (induced by source paper "towards amortized ranking critical training for collaborative filtering"):
  with members:
    - "eigenrec generalizing puresvd for effective and efficient top n recommendations"
    - "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"
    - "recurrent neural networks with top k gains for session based recommendations"

Connected through hyperedge HE-3 (induced by source paper "boosting item based collaborative filtering via nearly uncoupled random walks"):
  with members:
    - "eigenrec generalizing puresvd for effective and efficient top n recommendations"
    - "deep learning based recommender system a survey and new perspectives"
    - "adversarial personalized ranking for recommendation"
    - "deep item based collaborative filtering for top n recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "nais neural attentive item similarity model for recommendation"
    - "self attentive sequential recommendation"
    - "neural collaborative filtering"

Connected through hyperedge HE-4 (induced by source paper "a troubling analysis of reproducibility and progress in recommender systems research"):
  with members:
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "on the difficulty of evaluating baselines a study on recommender systems"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "attentive neural architecture incorporating song features for music recommendation"
    - "outer product based neural collaborative filtering"
    - "collaborative memory network for recommendation systems"
    - "spectral collaborative filtering"
    - "meta prod2vec product embeddings using side information for recommendation"

Connected through hyperedge HE-5 (induced by source paper "markov random fields for collaborative filtering"):
  with members:
    - "recurrent neural networks with top k gains for session based recommendations"
    - "neural collaborative filtering"
    - "wide deep learning for recommender systems"
    - "density estimation using real nvp"
    - "a neural autoregressive approach to collaborative filtering"
    - "session based recommendations with recurrent neural networks"
    - "made masked autoencoder for distribution estimation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "deep closed form subspace clustering"):
  with members:
    - "self supervised convolutional subspace clustering network"
    - "neural collaborative subspace clustering"
    - "scalable deep k subspace clustering"
    - "deep subspace clustering networks"
    - "robust multi body feature tracker a segmentation free approach"
    - "tensorflow a system for large scale machine learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-7 (induced by source paper "collaborative filtering via high dimensional regression"):
  with members:
    - "recurrent neural networks with top k gains for session based recommendations"
    - "recommendations as treatments debiasing learning and evaluation"
    - "distributed representations of words and phrases and their compositionality"

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)