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: "collaborative filtering with topic and social latent factors incorporating implicit feedback"
  Abstract: Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors-based collaborative filtering (CF) has become the popular approaches for RSs due to its accuracy and scalability. Recently, online social networks and user-generated content provide diverse sources for recommendation beyond ratings. Although social matrix factorization (Social MF) and topic matrix factorization (Topic MF) successfully exploit social relations and item reviews, respectively; both of them ignore some useful information. In this article, we investigate the effective data fusion by combining the aforementioned approaches. First, we propose a novel model MR3 to jointly model three sources of information (i.e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics. Second, we incorporate the implicit feedback from ratings into the proposed model to enhance its capability and to demonstrate its flexibility. We achieve more accurate rating prediction on real-life datasets over various state-of-the-art methods. Furthermore, we measure the contribution from each of the three data sources and the impact of implicit feedback from ratings, followed by the sensitivity analysis of hyperparameters. Empirical studies demonstrate the effectiveness and efficacy of our proposed model and its extension.

Connected through hyperedge HE-1 (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"
    - "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"
    - "transnets learning to transform for recommendation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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-3 (induced by source paper "a collaborative filtering model with heterogeneous neural networks for recommender systems"):
  with members:
    - "recurrent poisson factorization for temporal recommendation"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "hybrid recommender system based on autoencoders"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "conet collaborative cross networks for cross domain recommendation"):
  with members:
    - "integrating reviews into personalized ranking for cold start recommendation"
    - "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)