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 "sur featnet predicting the satisfied user ratio curvefor image compression with deep feature learning"):
  Abstract: The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, gives the distribution function of the Just Noticeable Difference (JND), the smallest distortion level that can be perceived by a subject when a reference image is compared to a distorted one. A sequence of JNDs can be defined with a suitable successive choice of reference images. We propose the first deep learning approach to predict SUR curves. We show how to exploit maximum likelihood estimation and the Kolmogorov-Smirnov test to select a suitable parametric model for the distribution function. We then use deep feature learning to predict samples of the SUR curve and apply the method of least squares to fit the parametric model to the predicted samples. Our deep learning approach relies on a Siamese Convolutional Neural Networks (CNN), transfer learning, and deep feature learning, using pairs consisting of a reference image and compressed image for training. Experiments on the MCL-JCI dataset showed state-of-the-art performance. For example, the mean Bhattacharyya distances between the predicted and ground truth first, second, and third JND distributions were 0.0810, 0.0702, and 0.0522, respectively, and the corresponding average absolute differences of the peak signal-to-noise ratio at the median of the distributions were 0.56, 0.65, and 0.53 dB.

Includes members:
  Member "koniq 10k an ecologically valid database for deep learning of blind image quality assessment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "no reference video quality assessment using multi level spatially pooled features")
  Member "effective aesthetics prediction with multi level spatially pooled features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting image aesthetic assessment via self supervised feature learning")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "videoset a large scale compressed video quality dataset based on jnd measurement":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient video classification using fewer frames")
  Member "prediction of satisfied user ratio for compressed video":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a user model for jnd based video quality assessment theory and applications")
  Member "mean deviation similarity index efficient and reliable full reference image quality evaluator":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment")
  Member "you only look once unified real time object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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.MM(Multimedia)