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: "do social explanations work studying and modeling the effects of social explanations in recommender systems"
  Abstract: Recommender systems associated with social networks often use social explanations (e.g. "X, Y and 2 friends like this") to support the recommendations. We present a study of the effects of these social explanations in a music recommendation context. We start with an experiment with 237 users, in which we show explanations with varying levels of social information and analyze their effect on users' decisions. We distinguish between two key decisions: the likelihood of checking out the recommended artist, and the actual rating of the artist based on listening to several songs. We find that while the explanations do have some influence on the likelihood, there is little correlation between the likelihood and actual (listening) rating for the same artist. Based on these insights, we present a generative probabilistic model that explains the interplay between explanations and background information on music preferences, and how that leads to a final likelihood rating for an artist. Acknowledging the impact of explanations, we discuss a general recommendation framework that models external informational elements in the recommendation interface, in addition to inherent preferences of users.

Connected through hyperedge HE-1 (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"
    - "transnets learning to transform for recommendation"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "explainable product search with a dynamic relation embedding model"):
  with members:
    - "conversational product search based on negative feedback"
    - "leverage implicit feedback for context aware product search"
    - "a zero attention model for personalized product search"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "learning a deep listwise context model for ranking refinement"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "learning over knowledge base embeddings for recommendation"
    - "explainable recommendation via multi task learning in opinionated text data"

Connected through hyperedge HE-3 (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-4 (induced by source paper "the fact taming latent factor models for explainability with factorization trees"):
  with members:
    - "explainable recommendation via multi task learning in opinionated text data"
    - "explainable recommendation a survey and new perspectives"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-5 (induced by source paper "explainable recommendation a survey and new perspectives"):
  with members:
    - "attention is not not explanation"
    - "the fact taming latent factor models for explainability with factorization trees"
    - "learning disentangled representations for recommendation"
    - "a capsule network for recommendation and explaining what you like and dislike"
    - "attention is not explanation"
    - "incorporating interpretability into latent factor models via fast influence analysis"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "mmalfm explainable recommendation by leveraging reviews and images"

Connected through hyperedge HE-6 (induced by source paper "ripplenet propagating user preferences on the knowledge graph for recommender systems"):
  with members:
    - "graphgan graph representation learning with generative adversarial nets"
    - "analogical inference for multi relational embeddings"
    - "wide deep learning for recommender systems"
    - "key value memory networks for directly reading documents"
    - "complex embeddings for simple link prediction"
    - "end to end memory networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-7 (induced by source paper "explainable recommendation via multi task learning in opinionated text data"):
  with members:
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "image based recommendations on styles and substitutes"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-8 (induced by source paper "providing explanations for recommendations in reciprocal environments"):
  with members:
    - "reciprocal recommendation system for online dating"

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.SI(Social and Information Networks)