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: "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
  Abstract: Incorporating knowledge graph (KG) into recommender system is promising in improving the recommendation accuracy and explainability. However, existing methods largely assume that a KG is complete and simply transfer the ”knowledge” in KG at the shallow level of entity raw data or embeddings. This may lead to suboptimal performance, since a practical KG can hardly be complete, and it is common that a KG has missing facts, relations, and entities. Thus, we argue that it is crucial to consider the incomplete nature of KG when incorporating it into recommender system.    In this paper, we jointly learn the model of recommendation and knowledge graph completion. Distinct from previous KG-based recommendation methods, we transfer the relation information in KG, so as to understand the reasons that a user likes an item. As an example, if a user has watched several movies directed by (relation) the same person (entity), we can infer that the director relation plays a critical role when the user makes the decision, thus help to understand the user's preference at a finer granularity.    Technically, we contribute a new translation-based recommendation model, which specially accounts for various preferences in translating a user to an item, and then jointly train it with a KG completion model by combining several transfer schemes. Extensive experiments on two benchmark datasets show that our method outperforms state-of-the-art KG-based recommendation methods. Further analysis verifies the positive effect of joint training on both tasks of recommendation and KG completion, and the advantage of our model in understanding user preference. We publish our project at https://github.com/TaoMiner/joint-kg-recommender.

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"
    - "reinforcement knowledge graph reasoning for explainable recommendation"

Connected through hyperedge HE-2 (induced by source paper "reinforced negative sampling over knowledge graph for recommendation"):
  with members:
    - "kgat knowledge graph attention network for recommendation"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "knowledge aware graph neural networks with label smoothness regularization for recommender systems"
    - "neural graph collaborative filtering"
    - "adversarial sampling and training for semi supervised information retrieval"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "attentive knowledge graph embedding for personalized recommendation"):
  with members:
    - "session based recommendation with graph neural network"
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "kgat knowledge graph attention network for recommendation"
    - "knowledge aware graph neural networks with label smoothness regularization for recommender systems"
    - "graph neural networks for social recommendation"
    - "knowledge graph convolutional networks for recommender systems"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"

Connected through hyperedge HE-4 (induced by source paper "neural graph collaborative filtering"):
  with members:
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "kgat knowledge graph attention network for recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "collaborative memory network for recommendation systems"
    - "representation learning on graphs with jumping knowledge networks"
    - "adversarial personalized ranking for recommendation"

Connected through hyperedge HE-5 (induced by source paper "personalized hashtag recommendation for micro videos"):
  with members:
    - "kgat knowledge graph attention network for recommendation"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "adversarial personalized ranking for recommendation"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "learning deep generative models of graphs"
    - "aspect aware latent factor model rating prediction with ratings and reviews"

Connected through hyperedge HE-6 (induced by source paper "relational collaborative filtering modeling multiple item relations for recommendation"):
  with members:
    - "neural graph collaborative filtering"
    - "recommendation through mixtures of heterogeneous item relationships"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "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"
    - "adversarial personalized ranking for recommendation"
    - "deep item based collaborative filtering for top n recommendation"

Connected through hyperedge HE-7 (induced by source paper "kgat knowledge graph attention network for recommendation"):
  with members:
    - "neural graph collaborative filtering"
    - "neural collective entity linking"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "representation learning on graphs with jumping knowledge networks"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"

Connected through hyperedge HE-8 (induced by source paper "low resource name tagging learned with weakly labeled data"):
  with members:
    - "few shot classification in named entity recognition task"
    - "improving neural relation extraction with implicit mutual relations"
    - "multi channel graph neural network for entity alignment"
    - "neural collective entity linking"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "few shot learning for named entity recognition in medical text"
    - "meta transfer learning for few shot 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)