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 "aesthetic based clothing recommendation"):
  Abstract: Recently, product images gain increasing concern in clothing recommendation since the visual appearance of the items have a significant impact on consumers’ decision. Existing models usually extract conventional features, such as convolutional neural network (CNN) features, scale-invariant feature transform (SIFT) features, and color histograms, to represent item image characters and capture user visual preferences. However, one important feature, aesthetic feature, is typically ignored. It is vital in recommendation since users’ decision depends largely on if the clothing is in line with their aesthetic while the conventional image features cannot portray this directly. To bridge this gap, we propose to introduce aesthetic information which is more related with users’ preference into the field of clothing recommender system. To do so, we first present the aesthetic features extracted by an pre-trained neural network, which is a brain inspired deep structure trained for aesthetic assessment task. Considering the aesthetic preference shows diversity with different people and time, we propose a novel tensor factorization model as a basic model and then incorporate the aesthetic features into it. Finally, extensive experiments on real-world datasets demonstrate that our approach can capture the aesthetic preference of consumers and outperform several state-of-the-art models significantly.

Includes members:
  Member "neural factorization machines for sparse predictive analytics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parameter efficient transfer from sequential behaviors for user profiling and recommendation")
  Member "neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advances in collaborative filtering and ranking")
  Member "item silk road recommending items from information domains to social users":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "fast matrix factorization for online recommendation with implicit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to hash with graph neural networks for recommender systems")
  Member "birank towards ranking on bipartite graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions")
  Member "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aesthetic image captioning from weakly labelled photographs")
  Member "a visual and textual recurrent neural network for sequential prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interest related item similarity model based on multimodal data for top n recommendation")
  Member "brain inspired deep networks for image aesthetics assessment":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep cross networks with aesthetic preference for cross domain recommendation")

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)