human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "an explainable autoencoder for collaborative filtering recommendation"):
  Abstract: Autoencoders are a common building block of Deep Learning architectures, where they are mainly used for representation learning. They have also been successfully used in Collaborative Filtering (CF) recommender systems to predict missing ratings. Unfortunately, like all black box machine learning models, they are unable to explain their outputs. Hence, while predictions from an Autoencoder-based recommender system might be accurate, it might not be clear to the user why a recommendation was generated. In this work, we design an explainable recommendation system using an Autoencoder model whose predictions can be explained using the neighborhood based explanation style. Our preliminary work can be considered to be the first step towards an explainable deep learning architecture based on Autoencoders.

Includes members:
  Member "visually explainable recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "hybrid collaborative recommendation via semi autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "next item recommendation with self attention")
  Member "automatic generation of natural language explanations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ai powered text generation for harmonious human machine interaction current state and future directions")
  Member "auto encoding user ratings via knowledge graphs in recommendation scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainable reasoning over knowledge graphs for recommendation")
  Member "capturing meaning in product reviews with character level generative text models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions")

Question: Please tell me which class the source paper 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)