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: "modeling the time varying subjective quality of http video streams with rate adaptations"
  Abstract: Newly developed hypertext transfer protocol (HTTP)-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' quality of experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality of a video as it is played, which is difficult due to hysteresis effects and nonlinearities in human behavioral responses. This paper presents a Hammerstein-Wiener model for predicting the time-varying subjective quality (TVSQ) of rate-adaptive videos. To collect data for model parameterization and validation, a database of longer duration videos with time-varying distortions was built and the TVSQs of the videos were measured in a large-scale subjective study. The proposed method is able to reliably predict the TVSQ of rate adaptive videos. Since the Hammerstein-Wiener model has a very simple structure, the proposed method is suitable for online TVSQ prediction in HTTP-based streaming.

Connected through hyperedge HE-1 (induced by source paper "convolutional neural networks for continuous qoe prediction in video streaming services"):
  with members:
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "modeling continuous video qoe evolution a state space approach"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "streaming video qoe modeling and prediction a long short term memory approach"
    - "self normalizing neural networks"
    - "an augmented autoregressive approach to http video stream quality prediction"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-2 (induced by source paper "a comparative evaluation of temporal pooling methods for blind video quality assessment"):
  with members:
    - "quality assessment of in the wild videos"
    - "perceptual quality prediction on authentically distorted images using a bag of features approach"

Connected through hyperedge HE-3 (induced by source paper "cumulative quality modeling for http adaptive streaming"):
  with members:
    - "streaming video qoe modeling and prediction a long short term memory approach"

Connected through hyperedge HE-4 (induced by source paper "streaming video qoe modeling and prediction a long short term memory approach"):
  with members:
    - "state space lstm models with particle mcmc inference"
    - "modeling continuous video qoe evolution a state space approach"
    - "show attend and tell neural image caption generation with visual attention"
    - "neural adaptive sequential monte carlo"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "sequence to sequence learning with neural networks"
    - "nova qoe driven optimization of dash based video delivery in networks"

Connected through hyperedge HE-5 (induced by source paper "an augmented autoregressive approach to http video stream quality prediction"):
  with members:
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"
    - "rate adaptation and admission control for video transmission with subjective quality constraints"

Connected through hyperedge HE-6 (induced by source paper "learning to predict streaming video qoe distortions rebuffering and memory"):
  with members:
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"

Connected through hyperedge HE-7 (induced by source paper "optimized packet scheduling in multiview video navigation systems"):
  with members:
    - "streaming video over http with consistent quality"
    - "correlation aware packet scheduling in multi camera networks"

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)