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: "recover subjective quality scores from noisy measurements"
  Abstract: Simple quality metrics such as PSNR are known to not correlate well with subjective quality when tested across a wide spectrum of video content or quality regime. Recently, efforts have been made in designing objective quality metrics trained on subjective data (e.g. VMAF), demonstrating better correlation with video quality perceived by human. Clearly, the accuracy of such a metric heavily depends on the quality of the subjective data that it is trained on. In this paper, we propose a new approach to recover subjective quality scores from noisy raw measurements, using maximum likelihood estimation, by jointly estimating the subjective quality of impaired videos, the bias and consistency of test subjects, and the ambiguity of video contents all together. We also derive closed-from expression for the confidence interval of each estimate. Compared to previous methods which partially exploit the subjective information, our approach is able to exploit the information in full, yielding tighter confidence interval and better handling of outliers without the need for z-scoring or subject rejection. It also handles missing data more gracefully. Finally, as side information, it provides interesting insights on the test subjects and video contents.

Connected through hyperedge HE-1 (induced by source paper "gpm a generic probabilistic model to recover annotator s behavior and ground truth labeling"):
  with members:
    - "a survey on truth discovery"

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"
    - "prediction of satisfied user ratio for compressed video"

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"
    - "prediction of satisfied user ratio for compressed video"

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)