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: "prediction of satisfied user ratio for compressed video"
  Abstract: A large-scale video quality dataset called the VideoSet has been constructed recently to measure human subjective experience of H.264 coded video in terms of the just-noticeable-difference (JND). It measures the first three JND points of 5-second video of resolution 1080p, 720p, 540p and 360p. Based on the VideoSet, we propose a method to predict the satisfied-user-ratio (SUR) curves using a machine learning framework. First, we partition a video clip into local spatial-temporal segments and evaluate the quality of each segment using the VMAF quality index. Then, we aggregate these local VMAF measures to derive a global one. Finally, the masking effect is incorporated and the support vector regression (SVR) is used to predict the SUR curves, from which the JND points can be derived. Experimental results are given to demonstrate the performance of the proposed SUR prediction method.

Connected through hyperedge HE-1 (induced by source paper "sur featnet predicting the satisfied user ratio curvefor image compression with deep feature learning"):
  with members:
    - "koniq 10k an ecologically valid database for deep learning of blind image quality assessment"
    - "effective aesthetics prediction with multi level spatially pooled features"
    - "focal loss for dense object detection"
    - "videoset a large scale compressed video quality dataset based on jnd measurement"
    - "mean deviation similarity index efficient and reliable full reference image quality evaluator"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (induced by source paper "a user model for jnd based video quality assessment theory and applications"):
  with members:
    - "a perceptually weighted rank correlation indicator for objective image quality assessment"
    - "a jnd based video quality assessment model and its application"
    - "videoset a large scale compressed video quality dataset based on jnd measurement"
    - "recover subjective quality scores from noisy measurements"

Connected through hyperedge HE-3 (induced by source paper "a jnd based video quality assessment model and its application"):
  with members:
    - "videoset a large scale compressed video quality dataset based on jnd measurement"
    - "recover subjective quality scores from noisy measurements"

Connected through hyperedge HE-4 (induced by source paper "analysis and prediction of jnd based video quality model"):
  with members:
    - "videoset a large scale compressed video quality dataset based on jnd measurement"

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.MM(Multimedia)