human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "soft video multicasting using adaptive compressed sensing"):
  Abstract: Recently, soft video multicasting has gained a lot of attention, especially in broadcast and mobile scenarios where the bit rate supported by the channel may differ across receivers, and may vary quickly over time. Unlike the conventional designs that force the source to use a single bit rate according to the receiver with the worst channel quality, soft video delivery schemes transmit the video such that the video quality at each receiver is commensurate with its specific instantaneous channel quality. In this paper, we present a soft video multicasting system using an adaptive block-based compressed sensing (BCS) method. The proposed system consists of an encoder, a transmission system, and a decoder. At the encoder side, each block in each frame of the input video is adaptively sampled with a rate that depends on the texture complexity and visual saliency of the block. The obtained BCS samples are then placed into several packets, and the packets are transmitted via a channel-aware OFDM (orthogonal frequency division multiplexing) transmission system with a number of subchannels. At the decoder side, the received BCS samples are first used to build an initial approximation of the transmitted frame. To further improve the reconstruction quality, an iterative BCS reconstruction algorithm is then proposed that uses an adaptive transform and an adaptive soft-thresholding operator, which exploits the temporal similarity between adjacent frames to achieve better reconstruction quality. The extensive objective and subjective experimental results indicate the superiority of the proposed system over the state-of-the-art soft video multicasting systems.

Includes members:
  Member "learning structured twin incoherent twin projective latent dictionary pairs for classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convolutional dictionary pair learning network for image representation learning")
  Member "scalable block diagonal locality constrained projective dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional dictionary pair learning network for image representation learning")
  Member "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matrix cofactorization for joint representation learning and supervised classification application to hyperspectral image analysis")
  Member "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")

Question: Please tell me which class the source paper 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)