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: "distortion bounds for source broadcast problem"
  Abstract: This paper investigates the joint source-channel coding problem of sending a memoryless source over a memoryless broadcast channel. An inner bound and several outer bounds on the admissible distortion region are derived, which respectively generalize and unify several existing bounds. As a consequence, we also obtain an inner bound and an outer bound for the degraded broadcast channel case. When specialized to the Gaussian or binary source broadcast, the inner bound and outer bound not only recover the best known inner bound and outer bound in the literature, but also generate some new results. Besides, we also extend the inner bound and outer bounds to the Wyner-Ziv source broadcast problem, i.e., source broadcast with side information available at decoders. Some new bounds are obtained when specialized to the Wyner-Ziv Gaussian and Wyner-Ziv binary cases.

Connected through hyperedge HE-1 (induced by source paper "joint source channel secrecy using uncoded schemes towards secure source broadcast"):
  with members:
    - "f divergence inequalities"
    - "source channel secrecy for shannon cipher system"
    - "distortion bounds for source broadcast over degraded channel"
    - "the henchman problem measuring secrecy by the minimum distortion in a list"
    - "hybrid digital analog codes for source channel broadcast of gaussian sources over gaussian channels"
    - "transmitting an analog gaussian source over a gaussian wiretap channel under snr mismatch"
    - "approximate characterizations for the gaussian source broadcast distortion region"

Connected through hyperedge HE-2 (induced by source paper "distortion bounds for transmitting correlated sources with common part over mac"):
  with members:
    - "distortion bounds for source broadcast over degraded channel"
    - "outer bounds on the admissible source region for broadcast channels with correlated sources"
    - "a new data processing inequality and its applications in distributed source and channel coding"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-3 (induced by source paper "comments on approximate characterizations for the gaussian source broadcast distortion region"):
  with members:
    - "distortion bounds for source broadcast over degraded channel"
    - "outer bounds on the admissible source region for broadcast channels with correlated sources"
    - "approximate characterizations for the gaussian source broadcast distortion region"

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)