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: "explainable recommendation a survey and new perspectives"
  Abstract: Explainable Recommendation refers to the personalized recommendation algorithms that address the problem of why -- they not only provide the user with the recommendations, but also make the user aware why such items are recommended by generating recommendation explanations, which help to improve the effectiveness, efficiency, persuasiveness, and user satisfaction of recommender systems. In recent years, a large number of explainable recommendation approaches -- especially model-based explainable recommendation algorithms -- have been proposed and adopted in real-world systems. #R##N#In this survey, we review the work on explainable recommendation that has been published in or before the year of 2018. We first high-light the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why. We then conduct a comprehensive survey of explainable recommendation itself in terms of three aspects: 1) We provide a chronological research line of explanations in recommender systems, including the user study approaches in the early years, as well as the more recent model-based approaches. 2) We provide a taxonomy for explainable recommendation algorithms, including user-based, item-based, model-based, and post-model explanations. 3) We summarize the application of explainable recommendation in different recommendation tasks, including product recommendation, social recommendation, POI recommendation, etc. We devote a chapter to discuss the explanation perspectives in the broader IR and machine learning settings, as well as their relationship with explainable recommendation research. We end the survey by discussing potential future research directions to promote the explainable recommendation research area.

Connected through hyperedge HE-1 (induced by source paper "a survey on knowledge graph based recommender systems"):
  with members:
    - "explainable knowledge graph based recommendation via deep reinforcement learning"
    - "deep short text classification with knowledge powered attention"
    - "research commentary on recommendations with side information a survey and research directions"
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "kgat knowledge graph attention network for recommendation"
    - "intentgc a scalable graph convolution framework fusing heterogeneous information for recommendation"
    - "an end to end neighborhood based interaction model for knowledge enhanced recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"

Connected through hyperedge HE-2 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems"):
  with members:
    - "jointly learning explainable rules for recommendation with knowledge graph"
    - "fairy a framework for understanding relationships between users actions and their social feeds"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "explainable recommendation via multi task learning in opinionated text data"
    - "aurora auditing pagerank on large graphs"

Connected through hyperedge HE-3 (induced by source paper "personalization of deep learning"):
  with members:
    - "human to ai coach improving human inputs to ai systems"
    - "personalized explanation in machine learning"
    - "transfer adaptation learning a decade survey"
    - "fairness through computationally bounded awareness"
    - "small sample learning in big data era"
    - "automated curriculum learning for neural networks"
    - "unsupervised domain adaptation for robust speech recognition via variational autoencoder based data augmentation"
    - "learning by asking questions"

Connected through hyperedge HE-4 (induced by source paper "explanation based tuning of opaque machine learners with application to paper recommendation"):
  with members:
    - "a scalable hybrid research paper recommender system for microsoft academic"
    - "scibert a pretrained language model for scientific text"
    - "the challenge of crafting intelligible intelligence"
    - "construction of the literature graph in semantic scholar"
    - "interpretable explorable approximations of black box models"
    - "a unified approach to interpreting model predictions"
    - "machine teaching a new paradigm for building machine learning systems"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-5 (induced by source paper "hybrid deep embedding for recommendations with dynamic aspect level explanations"):
  with members:
    - "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"
    - "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 "ethics of food recommender applications"):
  with members:
    - "the unfairness of popularity bias in recommendation"
    - "from what to how an overview of ai ethics tools methods and research to translate principles into practices"
    - "the ethics of ai ethics an evaluation of guidelines"
    - "from what to how an initial review of publicly available ai ethics tools methods and research to translate principles into practices"
    - "the global landscape of ai ethics guidelines"
    - "building jiminy cricket an architecture for moral agreements among stakeholders"

Connected through hyperedge HE-7 (induced by source paper "the future of misinformation detection new perspectives and trends"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "fighting fake news image splice detection via learned self consistency"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "detection and resolution of rumours in social media a survey"

Connected through hyperedge HE-8 (induced by source paper "towards knowledge based recommender dialog system"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "deep conversational recommender in travel"
    - "graph convolutional neural networks for web scale recommender systems"
    - "towards deep conversational recommendations"
    - "generating wikipedia by summarizing long sequences"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

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)