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: "context tree based image contour coding using a geometric prior"
  Abstract: Efficient encoding of object contours in images can facilitate advanced image/video compression techniques, such as shape-adaptive transform coding or motion prediction of arbitrarily shaped pixel blocks. We study the problem of lossless and lossy compression of detected contours in images. Specifically, we first convert a detected object contour into a sequence of directional symbols drawn from a small alphabet. To encode the symbol sequence using arithmetic coding, we compute an optimal variable-length context tree (VCT)    $\mathcal {T}$    via a maximum  a posterior  (MAP) formulation to estimate symbols’ conditional probabilities. MAP can avoid overfitting given a small training set    $\mathcal {X}$    of past symbol sequences by identifying a VCT    $\mathcal {T}$    with high likelihood    $P(\mathcal {X}|\mathcal {T})$    of observing    $\mathcal {X}$    given    $\mathcal {T}$   , using a geometric prior    $P(\mathcal {T})$    stating that image contours are more often straight than curvy. For the lossy case, we design fast dynamic programming (DP) algorithms that optimally trade off coding rate of an approximate contour    $\hat {\text {x}}$    given a VCT    $\mathcal {T}$    with two notions of distortion of    $\hat {\text {x}}$    with respect to the original contour x. To reduce the size of the DP tables, a total suffix tree is derived from a given VCT    $\mathcal {T}$    for compact table entry indexing, reducing complexity. Experimental results show that for lossless contour coding, our proposed algorithm outperforms state-of-the-art context-based schemes consistently for both small and large training datasets. For lossy contour coding, our algorithms outperform comparable schemes in the literature in rate–distortion performance.

Connected through hyperedge HE-1 (induced by source paper "joint denoising compression of image contours via shape prior and context tree"):
  with members:
    - "merge frame design for video stream switching using piecewise constant functions"
    - "optimal lagrange multipliers for dependent rate allocation in video coding"
    - "on prediction using variable order markov models"

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)