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: "integrating reviews into personalized ranking for cold start recommendation"
  Abstract: Item recommendation task predicts a personalized ranking over a set of items for each individual user. One paradigm is the rating-based methods that concentrate on explicit feedbacks and hence face the difficulties in collecting them. Meanwhile, the ranking-based methods are presented with rated items and then rank the rated above the unrated. This paradigm takes advantage of widely available implicit feedback. It, however, usually ignores a kind of important information: item reviews. Item reviews not only justify the preferences of users, but also help alleviate the cold-start problem that fails the collaborative filtering. In this paper, we propose two novel and simple models to integrate item reviews into Bayesian personalized ranking. In each model, we make use of text features extracted from item reviews using word embeddings. On top of text features we uncover the review dimensions that explain the variation in users' feedback and these review factors represent a prior preference of users. Experiments on six real-world data sets show the benefits of leveraging item reviews on ranking prediction. We also conduct analyses to understand the proposed models.

Connected through hyperedge HE-1 (induced by source paper "personalized neural embeddings for collaborative filtering with text"):
  with members:
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "collaborative memory network for recommendation systems"
    - "collaborative filtering with topic and social latent factors incorporating implicit feedback"
    - "lcmr local and centralized memories for collaborative filtering with unstructured text"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for recommendation"
    - "wide deep learning for recommender systems"
    - "a synthetic approach for recommendation combining ratings social relations and reviews"

Connected through hyperedge HE-2 (induced by source paper "data context adaptation for accurate recommendation with additional information"):
  with members:
    - "neural collaborative filtering"
    - "a comparative study of matrix factorization and random walk with restart in recommender systems"
    - "neural network matrix factorization"
    - "adam a method for stochastic optimization"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "conet collaborative cross networks for cross domain recommendation"):
  with members:
    - "collaborative filtering with topic and social latent factors incorporating implicit feedback"
    - "neural collaborative filtering"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "multi task self supervised visual learning"
    - "a survey on multi task learning"
    - "wide deep learning for recommender systems"
    - "cross stitch networks for multi task learning"
    - "a synthetic approach for recommendation combining ratings social relations and reviews"

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)