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: "sequence aware recommender systems"
  Abstract: Recommender systems are one of the most successful applications of data mining and machine learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process. In this work we review existing works that consider information from such sequentially-ordered user- item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems, and outline open challenges in the area.

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"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"
    - "neural collaborative ranking"

Connected through hyperedge HE-2 (induced by source paper "session based recommender systems for action selection in gui test generation"):
  with members:
    - "a deep learning based approach to automated android app testing"
    - "a survey on session based recommender systems"
    - "machine learning and evolutionary computing for gui based regression testing"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "generating realistic synthetic population datasets"
    - "a critical review of recurrent neural networks for sequence learning"
    - "session based recommendations with recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"

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 "parallel segregation integration networks for shared account cross domain sequential recommendations"):
  with members:
    - "conet collaborative cross networks for cross domain recommendation"
    - "repeatnet a repeat aware neural recommendation machine for session based recommendation"
    - "cross domain recommendation via deep domain adaptation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "generating personalized recipes from historical user preferences"):
  with members:
    - "discourse aware neural rewards for coherent text generation"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"

Connected through hyperedge HE-6 (induced by source paper "multi scale quasi rnn for next item recommendation"):
  with members:
    - "session based recommendation with graph neural network"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "multi pointer co attention networks for recommendation"
    - "a simple convolutional generative network for next item recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"

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"
    - "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"
    - "session aware information embedding for e commerce product recommendation"
    - "improving session recommendation with recurrent neural networks by exploiting dwell time"

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"
    - "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"
    - "contextual sequence modeling for recommendation with recurrent 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)