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: "combinatorial message sharing and a new achievable region for multiple descriptions"
  Abstract: This paper presents a new achievable rate-distortion region for the general L channel multiple descriptions problem. A well known general region for this problem is due to Venkataramani, Kramer and Goyal (VKG) [1]. Their encoding scheme is an extension of the El-Gamal-Cover (EC) and Zhang- Berger (ZB) coding schemes to the L channel case and includes a combinatorial number of refinement codebooks, one for each subset of the descriptions. As in ZB, the scheme also allows for a single common codeword to be shared by all descriptions. This paper proposes a novel encoding technique involving Combinatorial Message Sharing (CMS), where every subset of the descriptions may share a distinct common message. This introduces a combinatorial number of shared codebooks along with the refinement codebooks of [1]. We derive an achievable rate-distortion region for the proposed technique, and show that it subsumes the VKG region for general sources and distortion measures. We further show that CMS provides a strict improvement of the achievable region for any source and distortion measures for which some 2-description subset is such that ZB achieves points outside the EC region. We then show a more surprising result: CMS outperforms VKG for a general class of sources and distortion measures, including scenarios where the ZB and EC regions coincide for all 2-description subsets. In particular, we show that CMS strictly improves on VKG, for the L-channel quadratic Gaussian MD problem, for all L greater than or equal to 3, despite the fact that the EC region is complete for the corresponding 2-descriptions problem. Using the encoding principles derived, we show that the CMS scheme achieves the complete rate-distortion region for several asymmetric cross-sections of the L-channel quadratic Gaussian MD problem.

Connected through hyperedge HE-1 (induced by source paper "empirical coordination with multiple descriptions"):
  with members:
    - "empirical coordination subject to a fidelity criterion"
    - "information design for strategic coordination of autonomous devices with non aligned utilities"
    - "sequential empirical coordination under an output entropy constraint"
    - "empirical coordination in a triangular multiterminal network"

Connected through hyperedge HE-2 (induced by source paper "multiple description convolutional neural networks for image compression"):
  with members:
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning a virtual codec based on deep convolutional neural network to compress image"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep generative adversarial compression artifact removal"
    - "simultaneously color depth super resolution with conditional generative adversarial network"
    - "accurate image super resolution using very deep convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-3 (induced by source paper "the sum rate of vector gaussian multiple description coding with tree structured covariance distortion constraints"):
  with members:
    - "the gaussian many help one distributed source coding problem"
    - "approximating the gaussian multiple description rate region under symmetric distortion constraints"
    - "vector gaussian multiple description with individual and central receivers"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-4 (induced by source paper "quasi linear codes application to point to point and multi terminal source coding"):
  with members:
    - "an achievable rate distortion region for multiple descriptions source coding based on coset codes"
    - "achievable rate region for three user discrete broadcast channel based on coset codes"
    - "linear coding schemes for the distributed computation of subspaces"

Connected through hyperedge HE-5 (induced by source paper "an achievable rate distortion region for multiple descriptions source coding based on coset codes"):
  with members:
    - "an achievable rate distortion region for the multiple descriptions problem"
    - "achievable rate region for three user discrete broadcast channel based on coset codes"

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.IT(Information Theory)