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: "transnets learning to transform for recommendation"
  Abstract: Recently, deep learning methods have been shown to improve the performance of recommender systems over traditional methods, especially when review text is available. For example, a recent model, DeepCoNN, uses neural nets to learn one latent representation for the text of all reviews written by a target user, and a second latent representation for the text of all reviews for a target item, and then combines these latent representations to obtain state-of-the-art performance on recommendation tasks. We show that (unsurprisingly) much of the predictive value of review text comes from reviews of the target user for the target item. We then introduce a way in which this information can be used in recommendation, even when the target user's review for the target item is not available. Our model, called TransNets, extends the DeepCoNN model by introducing an additional latent layer representing the target user-target item pair. We then regularize this layer, at training time, to be similar to another latent representation of the target user's review of the target item. We show that TransNets and extensions of it improve substantially over the previous state-of-the-art.

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"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"

Connected through hyperedge HE-2 (induced by source paper "hybrid deep embedding for recommendations with dynamic aspect level explanations"):
  with members:
    - "explainable recommendation a survey and new perspectives"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "self attentive sequential recommendation"
    - "neural rating regression with abstractive tips generation for recommendation"
    - "joint deep modeling of users and items using reviews 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-3 (induced by source paper "deep neural review text interaction for recommendation systems"):
  with members:
    - "neural rating regression with abstractive tips generation for recommendation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "text matching as image recognition"
    - "inferring networks of substitutable and complementary products"
    - "image based recommendations on styles and substitutes"
    - "learning distributed representations from reviews for collaborative filtering"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-4 (induced by source paper "user diverse preference modeling by multimodal attentive metric learning"):
  with members:
    - "person re identification with metric learning using privileged information"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "metric factorization recommendation beyond matrix factorization"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "collaborative memory network for recommendation systems"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "neural collaborative filtering"

Connected through hyperedge HE-5 (induced by source paper "transfer meets hybrid a synthetic approach for cross domain collaborative filtering with text"):
  with members:
    - "conet collaborative cross networks for cross domain recommendation"
    - "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"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "a survey on multi task learning"

Connected through hyperedge HE-6 (induced by source paper "mmalfm explainable recommendation by leveraging reviews and images"):
  with members:
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "sherlock sparse hierarchical embeddings for visually aware one class collaborative filtering"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "vista a visually socially and temporally aware model for artistic recommendation"

Connected through hyperedge HE-7 (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"
    - "integrating reviews into personalized ranking for cold start recommendation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "wide deep learning for recommender systems"
    - "a synthetic approach for recommendation combining ratings social relations and reviews"

Connected through hyperedge HE-8 (induced by source paper "aspera aspect based rating prediction model"):
  with members:
    - "transrev modeling reviews as translations from users to items"
    - "using aspect extraction approaches to generate review summaries and user profiles"
    - "learning to generate reviews and discovering sentiment"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "representation of linguistic form and function in recurrent neural networks"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "image based recommendations on styles and substitutes"
    - "a primer on neural network models for natural language processing"

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)