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: "neural ir meets graph embedding a ranking model for product search"
  Abstract: Recently, neural models for information retrieval are becoming increasingly popular. They provide effective approaches for product search due to their competitive advantages in semantic matching. However, it is challenging to use graph-based features, though proved very useful in IR literature, in these neural approaches. In this paper, we leverage the recent advances in graph embedding techniques to enable neural retrieval models to exploit graph-structured data for automatic feature extraction. The proposed approach can not only help to overcome the long-tail problem of click-through data, but also incorporate external heterogeneous information to improve search results. Extensive experiments on a real-world e-commerce dataset demonstrate significant improvement achieved by our proposed approach over multiple strong baselines both as an individual retrieval model and as a feature used in learning-to-rank frameworks.

Connected through hyperedge HE-1 (induced by source paper "distilling structured knowledge into embeddings for explainable and accurate recommendation"):
  with members:
    - "jointly learning explainable rules for recommendation with knowledge graph"
    - "deep learning based recommender system a survey and new perspectives"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable recommendation a survey and new perspectives"
    - "neural factorization machines for sparse predictive analytics"
    - "deep mutual learning"
    - "neural collaborative filtering"
    - "heterogeneous information network embedding for recommendation"

Connected through hyperedge HE-2 (induced by source paper "document network embedding coping for missing content and missing links"):
  with members:
    - "global vectors for node representations"
    - "representation learning for recommender systems with application to the scientific literature"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

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)