human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  Abstract: Recommender Systems (RS) have became a popular research topic and, since 2016, Deep Learning methods and techniques have been increasingly explored in this area. News RS are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. The main contribution of this research was named CHAMELEON, a Deep Learning meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks. As information about users' past interactions is scarce in the news domain, the user context can be leveraged to deal with the user cold-start problem. Articles' content is also important to tackle the item cold-start problem. Additionally, the temporal decay of items (articles) relevance is very accelerated in the news domain. Furthermore, external breaking events may temporally attract global readership attention, a phenomenon generally known as concept drift in machine learning. All those characteristics are explicitly modeled on this research by a contextual hybrid session-based recommendation approach using Recurrent Neural Networks. The task addressed by this research is session-based news recommendation, i.e., next-click prediction using only information available in the current user session. A method is proposed for a realistic temporal offline evaluation of such task, replaying the stream of user clicks and fresh articles being continuously published in a news portal. Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based recommendation algorithms.

Includes members:
  Member "session based recommendation with graph neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph universal adversarial attacks a few bad actors ruin graph learning models")
  Member "contextual hybrid session based news recommendation with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the importance of news content representation in hybrid neural session based recommender systems")
  Member "on the importance of news content representation in hybrid neural session based recommender systems":
    also connected through other hyperedges:
      (none)
  Member "deep learning based recommender system a survey and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "autoemb automated embedding dimensionality search in streaming recommendations")
  Member "neurec on nonlinear transformation for personalized ranking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "collaborative filtering with a synthetic feedback loop")
  Member "sequence aware recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "session based recommender systems for action selection in gui test generation")
  Member "evaluation of session based recommendation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attentive item2vec neural attentive user representations")
  Member "multi pointer co attention networks for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cata a collaborative dual attentive autoencoder method for recommending scientific articles")

Question: Please tell me which class the source paper 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)