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: "visually explainable recommendation"
  Abstract: Images account for a significant part of user decisions in many application scenarios, such as product images in e-commerce, or user image posts in social networks. It is intuitive that user preferences on the visual patterns of image (e.g., hue, texture, color, etc) can be highly personalized, and this provides us with highly discriminative features to make personalized recommendations. #R##N#Previous work that takes advantage of images for recommendation usually transforms the images into latent representation vectors, which are adopted by a recommendation component to assist personalized user/item profiling and recommendation. However, such vectors are hardly useful in terms of providing visual explanations to users about why a particular item is recommended, and thus weakens the explainability of recommendation systems. #R##N#As a step towards explainable recommendation models, we propose visually explainable recommendation based on attentive neural networks to model the user attention on images, under the supervision of both implicit feedback and textual reviews. By this, we can not only provide recommendation results to the users, but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner. Experimental results show that our models are not only able to improve the recommendation performance, but also can provide persuasive visual explanations for the users to take the recommendations.

Connected through hyperedge HE-1 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "toward explainable fashion recommendation"
    - "explainable fashion recommendation a semantic attribute region guided approach"
    - "context aware visual compatibility prediction"
    - "regularizing black box models for improved interpretability"
    - "dressing as a whole outfit compatibility learning based on node wise graph neural networks"
    - "outfit compatibility prediction and diagnosis with multi layered comparison network"
    - "learning type aware embeddings for fashion compatibility"

Connected through hyperedge HE-2 (induced by source paper "an explainable autoencoder for collaborative filtering recommendation"):
  with members:
    - "hybrid collaborative recommendation via semi autoencoder"
    - "automatic generation of natural language explanations"
    - "auto encoding user ratings via knowledge graphs in recommendation scenarios"
    - "capturing meaning in product reviews with character level generative text models"

Connected through hyperedge HE-3 (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"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-4 (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"
    - "learning over knowledge base embeddings for recommendation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "personalized explanation in machine learning a conceptualization"):
  with members:
    - "explanation methods in deep learning users values concerns and challenges"
    - "interpretable to whom a role based model for analyzing interpretable machine learning systems"
    - "explainable recommendation a survey and new perspectives"
    - "technical privacy metrics a systematic survey"
    - "methods for interpreting and understanding deep neural networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "manipulating and measuring model interpretability"

Connected through hyperedge HE-6 (induced by source paper "personalized explanation in machine learning"):
  with members:
    - "explanation methods in deep learning users values concerns and challenges"
    - "interpretable to whom a role based model for analyzing interpretable machine learning systems"
    - "explainable recommendation a survey and new perspectives"
    - "technical privacy metrics a systematic survey"
    - "methods for interpreting and understanding deep neural networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "manipulating and measuring model interpretability"

Connected through hyperedge HE-7 (induced by source paper "explainable fashion recommendation a semantic attribute region guided approach"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning from history and present next item recommendation via discriminatively exploiting user behaviors"
    - "explainable recommendation a survey and new perspectives"
    - "aesthetic based clothing recommendation"
    - "a hierarchical attention model for social contextual image recommendation"
    - "visually aware fashion recommendation and design with generative image models"
    - "sherlock sparse hierarchical embeddings for visually aware one class collaborative filtering"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"

Connected through hyperedge HE-8 (induced by source paper "do co purchases reveal preferences explainable recommendation with attribute networks"):
  with members:
    - "explainable recommendation a survey and new perspectives"
    - "neural collaborative filtering"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "related pins at pinterest the evolution of a real world recommender system"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "image based recommendations on styles and substitutes"

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)