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: "learning over knowledge base embeddings for recommendation"
  Abstract: State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely neglected recently due to the availability of vast amount of data, and the learning power of many complex models. #R##N#However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors. A great challenge for using knowledge bases for recommendation is how to integrated large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements on knowledge base embedding sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge. In this work, we propose to reason over knowledge base embeddings for personalized recommendation. Specifically, we propose a knowledge base representation learning approach to embed heterogeneous entities for recommendation. Experimental results on real-world dataset verified the superior performance of our approach compared with state-of-the-art baselines.

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 "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"
    - "explainable recommendation via multi task learning in opinionated text data"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"

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"
    - "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"
    - "visually explainable recommendation"

Connected through hyperedge HE-4 (induced by source paper "explainable knowledge graph based recommendation via deep reinforcement learning"):
  with members:
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "session based social recommendation via dynamic graph attention networks"
    - "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"
    - "explainable recommendation a survey and new perspectives"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"

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"

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)