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 "the rate distortion function for product of two sources with side information at decoders"):
  Abstract: This paper investigates a lossy source coding problem in which two decoders can access their side-information respectively. The correlated sources are a product of two component correlated sources, and we exclusively investigate the case such that each component is degraded. We show the rate-distortion function for that case, and give the following observations. When the components are degraded in matched order, the rate distortion function of the product sources is equal to the sum of the component-wise rate distortion functions. On the otherhand, the former is strictly smaller than the latter when the component sources are degraded in mismatched order. The converse proof for the mismatched case is motivated by the enhancement technique used for broadcast channels. For binary Hamming and Gaussian examples, we evaluate the rate-distortion functions.

Includes members:
  Member "source coding problems with conditionally less noisy side information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "distributed hypothesis testing with concurrent detections")
  Member "lecture notes on network information theory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the required number of antennas in a point to point large but finite mimo system outage limited scenario")
  Member "universal source coding over generalized complementary delivery networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "universal wyner ziv coding for distortion constrained general side information")
  Member "side information scalable source coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (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)