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 attributes assessment of images"):
  Abstract: Image aesthetic quality assessment has been a relatively hot topic during the last decade. Most recently, comments type assessment (aesthetic captions) has been proposed to describe the general aesthetic impression of an image using text. In this paper, we propose Aesthetic Attributes Assessment of Images, which means the aesthetic attributes captioning. This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute. We introduce a new dataset named \emph{DPC-Captions} which contains comments of up to 5 aesthetic attributes of one image through knowledge transfer from a full-annotated small-scale dataset. Then, we propose Aesthetic Multi-Attribute Network (AMAN), which is trained on a mixture of fully-annotated small-scale PCCD dataset and weakly-annotated large-scale DPC-Captions dataset. Our AMAN makes full use of transfer learning and attention model in a single framework. The experimental results on our DPC-Captions and PCCD dataset reveal that our method can predict captions of 5 aesthetic attributes together with numerical score assessment of each attribute. We use the evaluation criteria used in image captions to prove that our specially designed AMAN model outperforms traditional CNN-LSTM model and modern SCA-CNN model of image captions.

Includes members:
  Member "discriminability objective for training descriptive captions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reasoning visual dialogs with structural and partial observations")
  Member "semstyle learning to generate stylised image captions using unaligned text":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "say as you wish fine grained control of image caption generation with abstract scene graphs")
  Member "nima neural image assessment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment")
  Member "neural aesthetic image reviewer":
    also connected through other hyperedges:
      (none)
  Member "predicting aesthetic score distribution through cumulative jensen shannon divergence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "composition aware image aesthetics assessment")
  Member "image aesthetic assessment an experimental survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "composition aware image aesthetics assessment")
  Member "convolutional image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  Member "deep aesthetic quality assessment with semantic information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "composition aware image aesthetics assessment")

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.CV(Computer Vision and Pattern Recognition)