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: "on the role of common codewords in quadratic gaussian multiple descriptions coding"
  Abstract: This paper focuses on the problem of $L-$channel quadratic Gaussian multiple description (MD) coding. We recently introduced a new encoding scheme in [1] for general $L-$channel MD problem, based on a technique called `Combinatorial Message Sharing' (CMS), where every subset of the descriptions shares a distinct common message. The new achievable region subsumes the most well known region for the general problem, due to Venkataramani, Kramer and Goyal (VKG) [2]. Moreover, we showed in [3] that the new scheme provides a strict improvement of the achievable region for any source and distortion measures for which some 2-description subset is such that the Zhang and Berger (ZB) scheme achieves points outside the El-Gamal and Cover (EC) region. In this paper, we show a more surprising result: CMS outperforms VKG for a general class of sources and distortion measures, which includes scenarios where for all 2-description subsets, the ZB and EC regions coincide. In particular, we show that CMS strictly extends VKG region, for the $L$-channel quadratic Gaussian MD problem for all $L\geq3$, 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, which have not been considered earlier.

Connected through hyperedge HE-1 (induced by source paper "combinatorial message sharing and a new achievable region for multiple descriptions"):
  with members:
    - "an achievable rate distortion region for the multiple descriptions problem"
    - "a strictly improved achievable region for multiple descriptions using combinatorial message sharing"
    - "asymmetric multilevel diversity coding and asymmetric gaussian multiple descriptions"
    - "approximating the gaussian multiple description rate region under symmetric distortion constraints"
    - "vector gaussian multiple description with individual and central receivers"

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)