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 topic regression with social matrix factorization for recommendation systems"
  Abstract: Social network websites, such as Facebook, YouTube, Lastfm etc, have become a popular platform for users to connect with each other and share content or opinions. They provide rich information for us to study the influence of user's social circle in their decision process. In this paper, we are interested in examining the effectiveness of social network information to predict the user's ratings of items. We propose a novel hierarchical Bayesian model which jointly incorporates topic modeling and probabilistic matrix factorization of social networks. A major advantage of our model is to automatically infer useful latent topics and social information as well as their importance to collaborative filtering from the training data. Empirical experiments on two large-scale datasets show that our algorithm provides a more effective recommendation system than the state-of-the art approaches. Our results reveal interesting insight that the social circles have more influence on people's decisions about the usefulness of information (e.g., bookmarking preference on Delicious) than personal taste (e.g., music preference on Lastfm). We also examine and discuss solutions on potential information leak in many recommendation systems that utilize social information.

Connected through hyperedge HE-1 (induced by source paper "semi supervised learning meets factorization learning to recommend with chain graph model"):
  with members:
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "a survey of multi view representation learning"):
  with members:
    - "word2visualvec cross media retrieval by visual feature prediction"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "word2visualvec image and video to sentence matching by visual feature prediction"
    - "convolutional two stream network fusion for video action recognition"
    - "deep cross modal hashing"

Connected through hyperedge HE-3 (induced by source paper "deep social collaborative filtering"):
  with members:
    - "deep adversarial social recommendation"
    - "graph neural networks for social recommendation"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "node2vec scalable feature learning for networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "learning continuous user representations through hybrid filtering with doc2vec"):
  with members:
    - "item2vec neural item embedding for collaborative filtering"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "from word embeddings to item recommendation"
    - "a flexible recommendation system for cable tv"
    - "skip thought vectors"
    - "e commerce in your inbox product recommendations at scale"
    - "distributed representations of sentences and documents"
    - "real time bidding benchmarking with ipinyou dataset"

Connected through hyperedge HE-5 (induced by source paper "d sempre learning deep semantic preserving embeddings for user interests social contents modeling"):
  with members:
    - "deep ctr prediction in display advertising"
    - "comparative deep learning of hybrid representations for image recommendations"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "fisher vectors derived from hybrid gaussian laplacian mixture models for image annotation"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "competitive analysis system for theatrical movie releases based on movie trailer deep video representation"):
  with members:
    - "collaborative metric learning recommendation system application to theatrical movie releases"
    - "youtube 8m a large scale video classification benchmark"
    - "going deeper with convolutions"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-7 (induced by source paper "collaborative metric learning recommendation system application to theatrical movie releases"):
  with members:
    - "wide deep learning for recommender systems"
    - "collaborative deep learning for recommender systems"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "collaborative filtering with topic and social latent factors incorporating implicit feedback"):
  with members:
    - "a synthetic approach for recommendation combining ratings social relations and reviews"
    - "empirical analysis of predictive algorithms for collaborative filtering"

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)