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: "rate distortion region of a gray wyner model with side information"
  Abstract: In this work, we establish a full single-letter characterization of the rate-distortion region of an instance of the Gray–Wyner model with side information at the decoders. Specifically, in this model, an encoder observes a pair of memoryless, arbitrarily correlated, sources #R##N#          #R##N#            #R##N#              #R##N#                (#R##N#                #R##N#                  S#R##N#                  1#R##N#                  n#R##N#                #R##N#                ,#R##N#                #R##N#                  S#R##N#                  2#R##N#                  n#R##N#                #R##N#                )#R##N#              #R##N#            #R##N#          #R##N#         and communicates with two receivers over an error-free rate-limited link of capacity #R##N#          #R##N#            #R##N#              #R##N#                R#R##N#                0#R##N#              #R##N#            #R##N#          #R##N#        , as well as error-free rate-limited individual links of capacities #R##N#          #R##N#            #R##N#              #R##N#                R#R##N#                1#R##N#              #R##N#            #R##N#          #R##N#         to the first receiver and #R##N#          #R##N#            #R##N#              #R##N#                R#R##N#                2#R##N#              #R##N#            #R##N#          #R##N#         to the second receiver. Both receivers reproduce the source component #R##N#          #R##N#            #R##N#              #R##N#                S#R##N#                2#R##N#                n#R##N#              #R##N#            #R##N#          #R##N#         losslessly; and Receiver 1 also reproduces the source component #R##N#          #R##N#            #R##N#              #R##N#                S#R##N#                1#R##N#                n#R##N#              #R##N#            #R##N#          #R##N#         lossily, to within some prescribed fidelity level #R##N#          #R##N#            #R##N#              #R##N#                D#R##N#                1#R##N#              #R##N#            #R##N#          #R##N#        . In addition, Receiver 1 and Receiver 2 are equipped, respectively, with memoryless side information sequences #R##N#          #R##N#            #R##N#              #R##N#                Y#R##N#                1#R##N#                n#R##N#              #R##N#            #R##N#          #R##N#         and #R##N#          #R##N#            #R##N#              #R##N#                Y#R##N#                2#R##N#                n#R##N#              #R##N#            #R##N#          #R##N#        . Important in this setup, the side information sequences are arbitrarily correlated among them, and with the source pair #R##N#          #R##N#            #R##N#              #R##N#                (#R##N#                #R##N#                  S#R##N#                  1#R##N#                  n#R##N#                #R##N#                ,#R##N#                #R##N#                  S#R##N#                  2#R##N#                  n#R##N#                #R##N#                )#R##N#              #R##N#            #R##N#          #R##N#        ; and are not assumed to exhibit any particular ordering. Furthermore, by specializing the main result to two Heegard–Berger models with successive refinement and scalable coding, we shed light on the roles of the common and private descriptions that the encoder should produce and the role of each of the common and private links. We develop intuitions by analyzing the developed single-letter rate-distortion regions of these models, and discuss some insightful binary examples.

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)