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: "videoset a large scale compressed video quality dataset based on jnd measurement"
  Abstract: A new methodology to measure coded image/video quality using the just-noticeable-difference (JND) idea was proposed. Several small JND-based image/video quality datasets were released by the Media Communications Lab at the University of Southern California. In this work, we present an effort to build a large-scale JND-based coded video quality dataset. The dataset consists of 220 5-second sequences in four resolutions (i.e., $1920 \times 1080$, $1280 \times 720$, $960 \times 540$ and $640 \times 360$). For each of the 880 video clips, we encode it using the H.264 codec with $QP=1, \cdots, 51$ and measure the first three JND points with 30+ subjects. The dataset is called the "VideoSet", which is an acronym for "Video Subject Evaluation Test (SET)". This work describes the subjective test procedure, detection and removal of outlying measured data, and the properties of collected JND data. Finally, the significance and implications of the VideoSet to future video coding research and standardization efforts are pointed out. All source/coded video clips as well as measured JND data included in the VideoSet are available to the public in the IEEE DataPort.

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"
    - "prediction of satisfied user ratio for compressed video"
    - "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 "efficient video classification using fewer frames"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "non local netvlad encoding for video classification"
    - "constrained size tensorflow models for youtube 8m video understanding challenge"
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "knowledge distillation by on the fly native ensemble"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "attention guided answer distillation for machine reading comprehension"
    - "learnable pooling methods for video classification"

Connected through hyperedge HE-3 (induced by source paper "quality assessment of in the wild videos"):
  with members:
    - "end to end learning of motion representation for video understanding"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "understanding how image quality affects deep neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "deep video color propagation"):
  with members:
    - "neural color transfer between images"
    - "the 2017 davis challenge on video object segmentation"
    - "photorealistic style transfer with screened poisson equation"
    - "deep photo style transfer"
    - "real time user guided image colorization with learned deep priors"
    - "video frame interpolation via adaptive separable convolution"
    - "universal style transfer via feature transforms"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-5 (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"
    - "prediction of satisfied user ratio for compressed video"
    - "recover subjective quality scores from noisy measurements"

Connected through hyperedge HE-6 (induced by source paper "deepqoe a unified framework for learning to predict video qoe"):
  with members:
    - "sensor fusion for robot control through deep reinforcement learning"
    - "neural machine translation by jointly learning to align and translate"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "a jnd based video quality assessment model and its application"):
  with members:
    - "prediction of satisfied user ratio for compressed video"
    - "recover subjective quality scores from noisy measurements"

Connected through hyperedge HE-8 (induced by source paper "analysis and prediction of jnd based video quality model"):
  with members:
    - "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)