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: "merge frame design for video stream switching using piecewise constant functions"
  Abstract: The ability to efficiently switch from one pre-encoded video stream to another (e.g., for bitrate adaptation or view switching) is important for many interactive streaming applications. Recently, stream-switching mechanisms based on distributed source coding (DSC) have been proposed. In order to reduce the overall transmission rate, these approaches provide a merge mechanism, where information is sent to the decoder, such that the exact same frame can be reconstructed given that any one of a known set of side information (SI) frames is available at the decoder (e.g., each SI frame may correspond to a different stream from which we are switching). However, the use of bit-plane coding and channel coding in many DSC approaches leads to complex coding and decoding. In this paper, we propose an alternative approach for merging multiple SI frames, using a piecewise constant (PWC) function as the merge operator. In our approach, for each block to be reconstructed, a series of parameters of these PWC merge functions are transmitted in order to guarantee identical reconstruction given the known SI blocks. We consider two different scenarios. In the first case, a target frame is first given, and then merge parameters are chosen, so that this frame can be reconstructed exactly at the decoder. In contrast, in the second scenario, the reconstructed frame and the merge parameters are jointly optimized to meet a rate-distortion criteria. Experiments show that for both scenarios, our proposed merge techniques can outperform both a recent approach based on DSC and the SP-frame approach in H.264, in terms of compression efficiency and decoder complexity.

Connected through hyperedge HE-1 (induced by source paper "mixed resolution image representation and compression with convolutional neural networks"):
  with members:
    - "an end to end compression framework based on convolutional neural networks"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning convolutional networks for content weighted image compression"
    - "full resolution image compression with recurrent neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "compression artifacts reduction by a deep convolutional network"

Connected through hyperedge HE-2 (induced by source paper "joint denoising compression of image contours via shape prior and context tree"):
  with members:
    - "context tree based image contour coding using a geometric prior"
    - "optimal lagrange multipliers for dependent rate allocation in video coding"
    - "on prediction using variable order markov models"

Connected through hyperedge HE-3 (induced by source paper "multi stream switching for interactive virtual reality video streaming"):
  with members:
    - "adaptive 360 vr video streaming based on mpeg dash srd"
    - "adaptive 360 vr video streaming divide and conquer"
    - "in network view synthesis for interactive multiview video systems"

Connected through hyperedge HE-4 (induced by source paper "optimal representations for adaptive streaming in interactive multi view video systems"):
  with members:
    - "optimal lagrange multipliers for dependent rate allocation in video coding"
    - "in network view synthesis for interactive multiview video systems"
    - "optimal layered representation for adaptive interactive multiview video streaming"
    - "optimal selection of adaptive streaming representations"
    - "optimized packet scheduling in multiview video navigation systems"

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)