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: "pseudo sequence based 2 d hierarchical coding structure for light field image compression"
  Abstract: In this paper, we propose a pseudo-sequence-based two-dimensional (2-D) hierarchical coding structure for light-field image compression. In the proposed scheme, we first decompose the light-field image into multiple views and organize them into a 2-D coding structure according to the spatial coordinates of the corresponding microlens. Then, we mainly develop three algorithms to optimize the 2-D coding structure. First, we propose a 2-D hierarchical coding structure with a limited number of reference frames to exploit the intercorrelations among various views. To be more specific, we divide all the views into four quadrants, and all the views are encoded one quadrant after another to reduce the reference buffer size as much as possible. Inside each quadrant, all the views are encoded hierarchically in both horizontal and vertical directions to fully exploit the correlations among different views. Second, we propose to use the distance between the current view and its reference views instead of the picture order count difference as the criterion for selecting better reference frames for each inter view. The distance-based criterion is also applied to the motion vector scaling process to obtain more accurate motion vector predictors. Third, an optimal bit allocation algorithm taking the influence of the various views on the following encoding views into account is proposed to further exploit the intercorrelations among various views and improve coding efficiency. The entire scheme is implemented in the reference software of high efficiency video coding. The experimental results demonstrate that the proposed novel pseudo-sequence-based 2-D hierarchical structure can achieve maximum 28.4% bit-rate savings compared with the previous pseudo-sequence-based light-field image compression method.

Connected through hyperedge HE-1 (induced by source paper "surface light field compression using a point cloud codec"):
  with members:
    - "light field reconstruction using shearlet transform"

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)