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: "trust aware top n recommender systems with correlative denoising autoencoder"
  Abstract: Both feedback of ratings and trust relationships can be used to reveal user preference to improve recommendation performance, especially for cold users. However, the high-order correlations between tow kind of data are always ignored by existing works. Towards this problem, we propose a Correlative Denoising Autoencoder (CoDAE) model to learn correlations from both rating and trust data for Top-N recommendation. First, a novel deep learning model CoDAE, in which two mid-layers from separate stack denoising autoencoders are fused into one shared layer. Advancing previous works which utilize these data in shallow level, this model can effectively extract high-order correlations from low-level representations of these data for recommendation. Second, to further learn implicit corrections between these two autoencoders, we develop a novel correlative regulation to build the relations between other hidden layers of the two separate autoencoders. In this way, this model can learn correlations more effectively and thus improve the recommendation quality. Comprehensive experiments on two public datasets demonstrate that the CoDAE significantly outperforms other state-of-the-art approaches in top-N recommendation task.

Connected through hyperedge HE-1 (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"
    - "pixie a system for recommending 3 billion items to 200 million users in real time"
    - "content based top n recommendation using heterogeneous relations"
    - "hybrid recommender system based on autoencoders"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "content based book recommending using learning for text categorization"

Connected through hyperedge HE-2 (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"
    - "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"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

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)