human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "rate distortion function for a heegard berger problem with two sources and degraded reconstruction sets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "source coding problems with conditionally less noisy side information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "biometric and physical identifiers with correlated noise for controllable private authentication")
  Member "rate distortion with side information at many decoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed hypothesis testing with concurrent detections")
  Member "on the capacity of the discrete memoryless broadcast channel with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploiting location information to enhance throughput in downlink v2i systems")
  Member "source coding for a simple network with receiver side information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "feedback coding schemes for the broadcast channel with mutual secrecy requirement")
  Member "side information scalable source coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "extended gray wyner system with complementary causal side information")
  Member "on multistage successive refinement for wyner ziv source coding with degraded side informations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vector gaussian successive refinement with degraded side information")

Question: Please tell me which class the source paper 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)