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: "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
  Abstract: Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting. The model generalizes recent advances in recurrent deep learning from i.i.d. input to non-i.i.d. (CF-based) input and provides a new denoising scheme along with a novel learnable pooling scheme for the recurrent autoencoder. To do this, we first develop a hierarchical Bayesian model for the DRAE and then generalize it to the CF setting. The synergy between denoising and CF enables CRAE to make accurate recommendations while learning to fill in the blanks in sequences. Experiments on real-world datasets from different domains (CiteULike and Netflix) show that, by jointly modeling the order-aware generation of sequences for the content information and performing CF for the ratings, CRAE is able to significantly outperform the state of the art on both the recommendation task based on ratings and the sequence generation task based on content information.

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "session based recommendation with graph neural network"
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"

Connected through hyperedge HE-2 (induced by source paper "coupled variational recurrent collaborative filtering"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "sequential variational autoencoders for collaborative filtering"
    - "a hybrid variational autoencoder for collaborative filtering"
    - "recurrent poisson factorization for temporal recommendation"
    - "neural collaborative filtering"
    - "discrete event continuous time rnns"
    - "streaming recommender systems"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "latent user linking for collaborative cross domain recommendation"):
  with members:
    - "cold start recommendations in collective matrix factorization"
    - "conet collaborative cross networks for cross domain recommendation"
    - "neural collaborative filtering"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "collaborative deep learning for recommender systems"
    - "efficient estimation of word representations in vector space"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "spectral collaborative filtering"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "mars memory attention aware recommender system"
    - "neural collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "neural autoregressive collaborative filtering for implicit feedback"

Connected through hyperedge HE-5 (induced by source paper "attribute aware collaborative filtering survey and classification"):
  with members:
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "on sampling strategies for neural network based collaborative filtering"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "multi view machines"
    - "vbpr visual bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-6 (induced by source paper "mars memory attention aware recommender system"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "interact and decide medley of sub attention networks for effective group recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-7 (induced by source paper "cross domain recommendation via deep domain adaptation"):
  with members:
    - "neural collaborative filtering"
    - "learning to remember rare events"
    - "neural autoregressive collaborative filtering for implicit feedback"
    - "a neural autoregressive approach to collaborative filtering"
    - "domain separation networks"
    - "lshtc a benchmark for large scale text classification"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "deepcci end to end deep learning for chemical chemical interaction prediction"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "generating focussed molecule libraries for drug discovery with recurrent neural networks"
    - "deepmirgene deep neural network based precursor microrna prediction"
    - "deeptarget end to end learning framework for microrna target prediction using deep recurrent neural networks"
    - "deep learning in bioinformatics"
    - "a critical review of recurrent neural networks for sequence learning"
    - "dna level splice junction prediction using deep recurrent neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)