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 filtering with recurrent neural networks"
  Abstract: We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We show that the LSTM is competitive in all aspects, and largely outperforms other methods in terms of item coverage and short term predictions.

Connected through hyperedge HE-1 (induced by source paper "two stage session based recommendations with candidate rank embeddings"):
  with members:
    - "session based recommendation with graph neural network"
    - "evaluation of session based recommendation algorithms"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "news session based recommendations using deep neural networks"
    - "session based recommendations with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-2 (induced by source paper "recurrent neural networks with top k gains for session based recommendations"):
  with members:
    - "recurrent latent variable networks for session based recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "theano a python framework for fast computation of mathematical expressions"
    - "session based recommendations with recurrent neural networks"
    - "blackout speeding up recurrent neural network language models with very large vocabularies"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "fast als based tensor factorization for context aware recommendation from implicit feedback"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-3 (induced by source paper "leveraging long and short term information in content aware movie recommendation"):
  with members:
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "generative adversarial text to image synthesis"
    - "mv rnn a multi view recurrent neural network for sequential recommendation"
    - "session based recommendations with recurrent neural networks"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "personalizing session based recommendations with hierarchical recurrent neural networks"):
  with members:
    - "improved recurrent neural networks for session based recommendations"
    - "a critical review of recurrent neural networks for sequence learning"
    - "session based recommendations with recurrent neural networks"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "fast als based tensor factorization for context aware recommendation from implicit feedback"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-5 (induced by source paper "use of deep learning in modern recommendation system a summary of recent works"):
  with members:
    - "hybrid deep semantic matrix factorization for tag aware personalized recommendation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "recurrent latent variable networks for session based recommendation"

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)