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: "session aware information embedding for e commerce product recommendation"
  Abstract: Most of the existing recommender systems assume that user's visiting history can be constantly recorded. However, in recent online services, the user identification may be usually unknown and only limited online user behaviors can be used. It is of great importance to model the temporal online user behaviors and conduct recommendation for the anonymous users. In this paper, we propose a list-wise deep neural network based architecture to model the limited user behaviors within each session. To train the model efficiently, we first design a session embedding method to pre-train a session representation, which incorporates different kinds of user search behaviors such as clicks and views. Based on the learnt session representation, we further propose a list-wise ranking model to generate the recommendation result for each anonymous user session. We conduct quantitative experiments on a recently published dataset from an e-commerce company. The evaluation results validate the effectiveness of the proposed method, which can outperform the state-of-the-art.

Connected through hyperedge HE-1 (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-2 (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"
    - "contextual sequence modeling for recommendation with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "personalizing graph neural networks with attention mechanism for session based recommendation"):
  with members:
    - "session based recommendation with graph neural network"
    - "constructing narrative event evolutionary graph for script event prediction"
    - "situation recognition with graph neural networks"
    - "inter session modeling for session based recommendation"
    - "attention is all you need"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "improved recurrent neural networks for session based recommendations"

Connected through hyperedge HE-4 (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"
    - "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"
    - "recurrent latent variable networks for session based recommendation"

Connected through hyperedge HE-5 (induced by source paper "rethinking the item order in session based recommendation with graph neural networks"):
  with members:
    - "session based recommendation with graph neural network"
    - "from zero shot learning to cold start recommendation"
    - "graph matching networks for learning the similarity of graph structured objects"
    - "a survey on session based recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "how powerful are graph neural networks"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "session based recommendation with graph neural network"):
  with members:
    - "constructing narrative event evolutionary graph for script event prediction"
    - "situation recognition with graph neural networks"
    - "neural attentive session based recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "the more you know using knowledge graphs for image classification"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "time weighted attentional session aware recommender system"):
  with members:
    - "towards neural mixture recommender for long range dependent user sequences"
    - "multi task learning for email search ranking with auxiliary query clustering"
    - "sequence aware recommender systems"
    - "recurrent neural networks for long and short term sequential recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "weakly supervised hierarchical text classification"
    - "mining entity synonyms with efficient neural set generation"
    - "improving session recommendation with recurrent neural networks by exploiting dwell time"

Connected through hyperedge HE-8 (induced by source paper "leverage implicit feedback for context aware product search"):
  with members:
    - "sequence aware recommender systems"
    - "learning a deep listwise context model for ranking refinement"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "on application of learning to rank for e commerce search"
    - "unbiased learning to rank with biased feedback"
    - "general factorization framework for context aware recommendations"

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)