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: "recurrent latent variable networks for session based recommendation"
  Abstract: In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques.

Connected through hyperedge HE-1 (induced by source paper "a survey on session based recommender systems"):
  with members:
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "session aware information embedding for e commerce product recommendation"
    - "leveraging long and short term information in content aware movie recommendation"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "joint neural collaborative filtering for recommender systems"):
  with members:
    - "neural graph collaborative filtering"
    - "deep learning based recommender system a survey and new perspectives"
    - "attention based hierarchical neural query suggestion"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "joint deep modeling of users and items using reviews for recommendation"

Connected through hyperedge HE-3 (induced by source paper "a simple convolutional generative network for next item recommendation"):
  with members:
    - "adversarial personalized ranking for recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "neural factorization machines for sparse predictive analytics"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "convolutional sequence to sequence learning"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "context tree for adaptive session based recommendation"):
  with members:
    - "evaluation of session based recommendation algorithms"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "session based recommendations with recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "personalized news recommendation with context trees"

Connected through hyperedge HE-5 (induced by source paper "recurrent neural networks with top k gains for session based recommendations"):
  with members:
    - "improved recurrent neural networks for session based recommendations"
    - "theano a python framework for fast computation of mathematical expressions"
    - "collaborative filtering with recurrent neural networks"
    - "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-6 (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"
    - "collaborative filtering with recurrent neural networks"

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)