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: "an achievable rate distortion region for multiple descriptions source coding based on coset codes"
  Abstract: We consider the problem of multiple descriptions (MD) source coding and propose new coding strategies involving both unstructured and structured coding layers. Previously, the most general achievable rate-distortion (RD) region for the $l$-descriptions problem was the Combinatorial Message Sharing with Binning (CMSB) region. The CMSB scheme utilizes unstructured quantizers and unstructured binning. In the first part of the paper, we show that this strategy can be improved upon using more general unstructured quantizers and a more general unstructured binning method. In the second part, structured coding strategies are considered. First, structured coding strategies are developed by considering specific MD examples involving three or more descriptions. We show that application of structured quantizers results in strict RD improvements when there are more than two descriptions. Furthermore, we show that structured binning also yields improvements. These improvements are in addition to the ones derived in the first part of the paper. This suggests that structured coding is essential when coding over more than two descriptions. Using the ideas developed through these examples we provide a new unified coding strategy by considering several structured coding layers. Finally, we characterize its performance in the form of an inner bound to the optimal rate-distortion region using computable single-letter information quantities. The new RD region strictly contains all of the previous known achievable regions.

Connected through hyperedge HE-1 (induced by source paper "deep optimized multiple description image coding via scalar quantization learning"):
  with members:
    - "dmcnn dual domain multi scale convolutional neural network for compression artifacts removal"
    - "the perception distortion tradeoff"
    - "conditional probability models for deep image compression"
    - "multiple description convolutional neural networks for image compression"
    - "learning a virtual codec based on deep convolutional neural network to compress image"
    - "understanding convolution for semantic segmentation"
    - "learning convolutional networks for content weighted image compression"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "trade off between communication and cooperation in the interference channel"):
  with members:
    - "sub optimality of the han kobayashi achievable region for interference channels"
    - "an achievable rate distortion region for the multiple descriptions problem"
    - "on interference networks over finite fields"
    - "achievable rate region for three user discrete broadcast channel based on coset codes"
    - "the approximate capacity of the many to one and one to many gaussian interference channels"

Connected through hyperedge HE-3 (induced by source paper "quasi linear codes application to point to point and multi terminal source coding"):
  with members:
    - "combinatorial message sharing and a new achievable region for multiple descriptions"
    - "achievable rate region for three user discrete broadcast channel based on coset codes"
    - "linear coding schemes for the distributed computation of subspaces"

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)