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: "autosvd an efficient hybrid collaborative filtering model via contractive auto encoders"
  Abstract: Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based filtering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-crafted feature engineering, which is usually noise-prone and biased by different feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational efficiency, which jointly models content information as representations of effectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large-scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation.

Connected through hyperedge HE-1 (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-2 (induced by source paper "quaternion collaborative filtering for recommendation"):
  with members:
    - "deeprec an open source toolkit for deep learning based recommendation"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "next item recommendation with self attention"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"

Connected through hyperedge HE-3 (induced by source paper "latent relational metric learning via memory based attention for collaborative ranking"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "multi pointer co attention networks for recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "a compare propagate architecture with alignment factorization for natural language inference"
    - "neural collaborative filtering"
    - "translation based recommendation"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"
    - "fast matrix factorization for online recommendation with implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "sequential variational autoencoders for collaborative filtering"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "sequence aware recommender systems"
    - "a hybrid variational autoencoder for collaborative filtering"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "lstm a search space odyssey"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "translation based recommendation"

Connected through hyperedge HE-5 (induced by source paper "a hierarchical attention model for social contextual image recommendation"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "nais neural attentive item similarity model for recommendation"
    - "neural collaborative filtering"
    - "a survey on network embedding"
    - "related pins at pinterest the evolution of a real world recommender system"
    - "a context aware attention network for interactive question answering"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "vista a visually socially and temporally aware model for artistic recommendation"

Connected through hyperedge HE-6 (induced by source paper "collaborative memory network for recommendation systems"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "neural collaborative filtering"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "deep memory networks for attitude identification"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "wide deep learning for recommender systems"
    - "dynamic memory networks for visual and textual question answering"
    - "end to end memory networks"

Connected through hyperedge HE-7 (induced by source paper "optimization matrix factorization recommendation algorithm based on rating centrality"):
  with members:
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "coupled item based matrix factorization"

Connected through hyperedge HE-8 (induced by source paper "hybrid collaborative recommendation via semi autoencoder"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "hybrid recommender system based on autoencoders"
    - "collaborative deep learning for recommender systems"
    - "bpr bayesian personalized ranking from implicit feedback"

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)