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 "explainable fashion recommendation a semantic attribute region guided approach"):
  Abstract: In fashion recommender systems, each product usually consists of multiple semantic attributes (e.g., sleeves, collar, etc). When making cloth decisions, people usually show preferences for different semantic attributes (e.g., the clothes with v-neck collar). Nevertheless, most previous fashion recommendation models comprehend the clothing images with a global content representation and lack detailed understanding of users' semantic preferences, which usually leads to inferior recommendation performance. To bridge this gap, we propose a novel Semantic Attribute Explainable Recommender System (SAERS). Specifically, we first introduce a fine-grained interpretable semantic space. We then develop a Semantic Extraction Network (SEN) and Fine-grained Preferences Attention (FPA) module to project users and items into this space, respectively. With SAERS, we are capable of not only providing cloth recommendations for users, but also explaining the reason why we recommend the cloth through intuitive visual attribute semantic highlights in a personalized manner. Extensive experiments conducted on real-world datasets clearly demonstrate the effectiveness of our approach compared with the state-of-the-art methods.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "learning from history and present next item recommendation via discriminatively exploiting user behaviors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for sequential recommendation algorithms influential factors and evaluations")
  Member "explainable recommendation a survey and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "visually explainable recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "aesthetic based clothing recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modeling embedding dimension correlations via convolutional neural collaborative filtering")
  Member "a hierarchical attention model for social contextual image recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "diffnet a neural influence and interest diffusion network for social recommendation")
  Member "visually aware fashion recommendation and design with generative image models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning diverse fashion collocation by neural graph filtering")
  Member "sherlock sparse hierarchical embeddings for visually aware one class collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dressing as a whole outfit compatibility learning based on node wise graph neural networks")

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)