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: "minimum expected distortion in gaussian source coding with fading side information"
  Abstract: An encoder, subject to a rate constraint, wishes to describe a Gaussian source under squared-error distortion. The decoder, besides receiving the encoder's description, also observes side information consisting of uncompressed source symbol subject to slow fading and noise. The decoder knows the fading realization but the encoder knows only its distribution. The rate-distortion function that simultaneously satisfies the distortion constraints for all fading states was derived by Heegard and Berger. A layered encoding strategy is considered in which each codeword layer targets a given fading state. When the side-information channel has two discrete fading states, the expected distortion is minimized by optimally allocating the encoding rate between the two codeword layers. For multiple fading states, the minimum expected distortion is formulated as the solution of a convex optimization problem with linearly many variables and constraints. Through a limiting process on the primal and dual solutions, it is shown that single-layer rate allocation is optimal when the fading probability density function is continuous and quasiconcave (e.g., Rayleigh, Rician, Nakagami, and log-normal). In particular, under Rayleigh fading, the optimal single codeword layer targets the least favorable state as if the side information was absent.

Connected through hyperedge HE-1 (induced by source paper "robust baseband compression against congestion in packet based fronthaul networks using multiple description coding"):
  with members:
    - "hybrid data sharing and compression strategy for downlink cloud radio access network"
    - "reliable and low latency fronthaul for tactile internet applications"
    - "fronthaul compression and transmit beamforming optimization for multi antenna uplink c ran"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "robust and efficient distributed compression for cloud radio access networks"

Connected through hyperedge HE-2 (induced by source paper "joint source channel coding with time varying channel and side information"):
  with members:
    - "joint source channel coding with correlated interference"
    - "wyner ziv coding over broadcast channels digital schemes"
    - "distortion minimization in gaussian layered broadcast coding with successive refinement"
    - "joint source channel coding with side information using hybrid digital analog codes"
    - "on the distortion snr exponent of some layered transmission schemes"
    - "joint source channel codes for mimo block fading channels"

Connected through hyperedge HE-3 (induced by source paper "robust and efficient distributed compression for cloud radio access networks"):
  with members:
    - "quantized compute and forward a low complexity architecture for distributed antenna systems"
    - "uplink comp under a constrained backhaul and imperfect channel knowledge"
    - "successive wyner ziv coding scheme and its application to the quadratic gaussian ceo problem"
    - "distributed mimo receiver achievable rates and upper bounds"

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)