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: "contextual sequence modeling for recommendation with recurrent neural networks"
  Abstract: Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models can provide useful user representations for recommendation. However, current RNN modeling approaches summarize the user state by only taking into account the sequence of items that the user has interacted with in the past, without taking into account other essential types of context information such as the associated types of user-item interactions, the time gaps between events and the time of day for each interaction. To address this, we propose a new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and more explicitly, in the model dynamics, by parametrizing the hidden unit transitions as a function of context information. We compare our CRNNs approach with RNNs and non-sequential baselines and show good improvements on the next event prediction task.

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "session based recommendation with graph neural network"
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"

Connected through hyperedge HE-2 (induced by source paper "future data helps training modeling future contexts for session based recommendation"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "mass masked sequence to sequence pre training for language generation"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "cosrec 2d convolutional neural networks for sequential recommendation"
    - "hierarchical gating networks for sequential recommendation"
    - "deep contextualized word representations"
    - "next item recommendation with self attention"
    - "a simple convolutional generative network for next item recommendation"

Connected through hyperedge HE-3 (induced by source paper "deep learning for sequential recommendation algorithms influential factors and evaluations"):
  with members:
    - "session based recommendation with graph neural network"
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "learning to recommend with multiple cascading behaviors"
    - "research commentary on recommendations with side information a survey and research directions"
    - "improving end to end sequential recommendations with intent aware diversification"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "a survey on session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "modeling the past and future contexts for session based recommendation"):
  with members:
    - "deep learning based sequential recommender systems concepts algorithms and evaluations"
    - "mass masked sequence to sequence pre training for language generation"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "cosrec 2d convolutional neural networks for sequential recommendation"
    - "hierarchical gating networks for sequential recommendation"
    - "a survey on session based recommender systems"
    - "adversarial personalized ranking for recommendation"

Connected through hyperedge HE-5 (induced by source paper "towards neural mixture recommender for long range dependent user sequences"):
  with members:
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "a simple convolutional generative network for next item recommendation"
    - "top k off policy correction for a reinforce recommender system"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a brand level ranking system with the customized attention gru model"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "session aware information embedding for e commerce product recommendation"

Connected through hyperedge HE-6 (induced by source paper "quantifying long range dependence in language and user behavior to improve rnns"):
  with members:
    - "towards neural mixture recommender for long range dependent user sequences"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "dilated recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-7 (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"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "recurrent latent variable networks for session based recommendation"

Connected through hyperedge HE-8 (induced by source paper "contextual hybrid session based news recommendation with recurrent neural networks"):
  with members:
    - "session based recommendation with graph neural network"
    - "deep learning based recommender system a survey and new perspectives"
    - "sequence aware recommender systems"
    - "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"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical 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)