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: "a new compressive video sensing framework for mobile broadcast"
  Abstract: A new video coding method based on compressive sampling is proposed. In this method, a video is coded using compressive measurements on video cubes. Video reconstruction is performed by minimization of total variation (TV) of the pixelwise discrete cosine transform coefficients along the temporal direction. A new reconstruction algorithm is developed from TVAL3, an efficient TV minimization algorithm based on the alternating minimization and augmented Lagrangian methods. Video coding with this method is inherently scalable, and has applications in mobile broadcast.

Connected through hyperedge HE-1 (induced by source paper "a framework for super resolution of scalable video via sparse reconstruction of residual frames"):
  with members:
    - "lisens a scalable architecture for video compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "vlsi friendly framework for scalable video coding based on compressed sensing"):
  with members:
    - "message passing algorithms for compressed sensing"
    - "message passing algorithms for compressed sensing ii analysis and validation"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "lensless compressive imaging"):
  with members:
    - "compressive hyperspectral imaging with side information"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "bayesian nonparametric dictionary learning for compressed sensing mri"
    - "from denoising to compressed sensing"
    - "lensless imaging by compressive sensing"
    - "multi view in lensless compressive imaging"
    - "surveillance video processing using compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "signal to noise ratio in lensless compressive imaging"):
  with members:
    - "lensless imaging by compressive sensing"
    - "multi view in lensless compressive imaging"
    - "lensless compressive sensing imaging"
    - "surveillance video processing using compressive sensing"

Connected through hyperedge HE-5 (induced by source paper "noise analysis for lensless compressive imaging"):
  with members:
    - "lensless imaging by compressive sensing"
    - "multi view in lensless compressive imaging"
    - "lensless compressive sensing imaging"
    - "surveillance video processing using compressive sensing"

Connected through hyperedge HE-6 (induced by source paper "compressive measurements generated by structurally random matrices asymptotic normality and quantization"):
  with members:
    - "fast and efficient compressive sensing using structurally random matrices"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"

Connected through hyperedge HE-7 (induced by source paper "adaptive low rank and sparse decomposition of video using compressive sensing"):
  with members:
    - "lensless imaging by compressive sensing"
    - "surveillance video processing using compressive sensing"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "multi view in lensless compressive imaging"):
  with members:
    - "lensless imaging by compressive sensing"
    - "surveillance video processing using compressive sensing"

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)