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: "a synthetic approach for recommendation combining ratings social relations and reviews"
  Abstract: Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices. Online social networks and user-generated content provide diverse sources for recommendation beyond ratings, which present opportunities as well as challenges for traditional RSs. Although social matrix factorization (Social MF) can integrate ratings with social relations and topic matrix factorization can integrate ratings with item reviews, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the two approaches, in two steps. First, we extend Social MF to exploit the graph structure of neighbors. Second, we propose a novel framework MR3 to jointly model these three types of information effectively for rating prediction by aligning latent factors and hidden topics. We achieve more accurate rating prediction on two real-life datasets. Furthermore, we measure the contribution of each data source to the proposed framework.

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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "collaborative filtering with topic and social latent factors incorporating implicit feedback"):
  with members:
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "collaborative topic regression with social matrix factorization for recommendation systems"

Connected through hyperedge HE-4 (induced by source paper "integrating reviews into personalized ranking for cold start recommendation"):
  with members:
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "image based recommendations on styles and substitutes"
    - "distributed representations of words and phrases and their compositionality"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "learning item trees for probabilistic modelling of implicit feedback"

Connected through hyperedge HE-5 (induced by source paper "extracting implicit social relation for social recommendation techniques in user rating prediction"):
  with members:
    - "learning optimal social dependency for recommendation"

Connected through hyperedge HE-6 (induced by source paper "learning optimal social dependency for recommendation"):
  with members:
    - "a convex formulation for learning task relationships in multi task learning"

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)