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: "aspect aware latent factor model rating prediction with ratings and reviews"
  Abstract: Although latent factor model (e.g., matrix factorization) achieves good accuracy in rating prediction, it suffers the problems including cold-start, non-transparency, and suboptimal recommendation for local users or items. In this paper, we exploit textual review information with ratings to tackle these limitations. Firstly, we apply a proposed aspect-aware topic model (ATM) on the review text to model user preferences and item features from different aspects, and estimate the aspect importance of a user towards an item. The aspect importance is then integrated into a novel aspect-aware latent factor model (ALFM), which learns user’s and item’s latent factors based on ratings. In particular, ALFM introduces a weighted matrix to associate those latent factors with the same set of aspects discovered by ATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, an aspect rating is weighted by an aspect importance, which is dependent on the targeted user’s preferences and targeted item’s features. Therefore, it is expected that the proposed method can model a user’s preferences on an item more accurately for each user-item pair locally. Comprehensive experimental studies have been conducted on 19 datasets from Amazon and  Yelp 2017 Challenge dataset. Results show that our method achieves significant improvement compared with strong baseline methods, especially for users with only few ratings. Moreover, our model could interpret the recommendation results in depth.

Connected through hyperedge HE-1 (induced by source paper "recommender systems based on generative adversarial networks a problem driven perspective"):
  with members:
    - "deep adversarial social recommendation"
    - "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation"
    - "kgat knowledge graph attention network for recommendation"
    - "deep learning to address candidate generation and cold start challenges in recommender systems a research survey"
    - "generating reliable friends via adversarial training to improve social recommendation"
    - "cold start playlist recommendation with multitask learning"
    - "exs explainable search using local model agnostic interpretability"
    - "adversarial collaborative auto encoder for top n recommendation"

Connected through hyperedge HE-2 (induced by source paper "reinforced negative sampling over knowledge graph for recommendation"):
  with members:
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "knowledge aware graph neural networks with label smoothness regularization for recommender systems"
    - "neural graph collaborative filtering"
    - "adversarial sampling and training for semi supervised information retrieval"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems"):
  with members:
    - "deep conversational recommender in travel"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "toward building conversational recommender systems a contextual bandit approach"
    - "towards deep conversational recommendations"
    - "learning contextual bandits in a non stationary environment"
    - "collaborative memory network for recommendation systems"
    - "explicit state tracking with semi supervisionfor neural dialogue generation"
    - "conversational recommender system"

Connected through hyperedge HE-4 (induced by source paper "hotelrec a novel very large scale hotel recommendation dataset"):
  with members:
    - "multi dimensional explanation of reviews"
    - "neural graph collaborative filtering"
    - "neural collaborative filtering"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "understanding neural networks through representation erasure"
    - "image based recommendations on styles and substitutes"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-5 (induced by source paper "hybrid deep embedding for recommendations with dynamic aspect level explanations"):
  with members:
    - "explainable recommendation a survey and new perspectives"
    - "self attentive sequential recommendation"
    - "neural rating regression with abstractive tips generation for recommendation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for recommendation"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "do social explanations work studying and modeling the effects of social explanations in recommender systems"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "neural graph collaborative filtering"):
  with members:
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "graph convolutional neural networks for web scale recommender systems"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "collaborative memory network for recommendation systems"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-7 (induced by source paper "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "neural collective entity linking"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "learning over knowledge base embeddings for recommendation"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "adversarial personalized ranking for recommendation"
    - "explicit state tracking with semi supervisionfor neural dialogue generation"

Connected through hyperedge HE-8 (induced by source paper "personalized hashtag recommendation for micro videos"):
  with members:
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "adversarial personalized ranking for recommendation"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "learning deep generative models of graphs"

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)