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: "a connection between good rate distortion codes and backward dmcs"
  Abstract: Let $X^n\in\mathcal{X}^n$ be a sequence drawn from a discrete memoryless source, and let $Y^n\in\mathcal{Y}^n$ be the corresponding reconstruction sequence that is output by a good rate-distortion code. This paper establishes a property of the joint distribution of $(X^n,Y^n)$. It is shown that for $D>0$, the input-output statistics of a $R(D)$-achieving rate-distortion code converge (in normalized relative entropy) to the output-input statistics of a discrete memoryless channel (dmc). The dmc is "backward" in that it is a channel from the reconstruction space $\mathcal{Y}^n$ to source space $\mathcal{X}^n$. It is also shown that the property does not necessarily hold when normalized relative entropy is replaced by variational distance.

Connected through hyperedge HE-1 (induced by source paper "the likelihood encoder for lossy compression"):
  with members:
    - "joint source channel secrecy using hybrid coding"
    - "a rate distortion based secrecy system with side information at the decoders"
    - "a generalized typicality for abstract alphabets"
    - "the likelihood encoder for lossy source compression"
    - "a technique for deriving one shot achievability results in network information theory"
    - "non asymptotic output statistics of random binning and its applications"
    - "the likelihood encoder for source coding"
    - "distributed channel synthesis"

Connected through hyperedge HE-2 (induced by source paper "the rate distortion risk in estimation from compressed data"):
  with members:
    - "the distortion rate function of sampled wiener processes"
    - "optimality guarantees for distributed statistical estimation"
    - "mismatched codebooks and the role of entropy coding in lossy data compression"

Connected through hyperedge HE-3 (induced by source paper "mismatched multiterminal source coding"):
  with members:
    - "indirect rate distortion function of a binary i i d source"

Connected through hyperedge HE-4 (induced by source paper "distributed channel synthesis"):
  with members:
    - "secure cascade channel synthesis"
    - "on secure communication with constrained randomization"
    - "achievability proof via output statistics of random binning"
    - "channel simulation via interactive communications"
    - "coordination via a relay"
    - "secrecy is cheap if the adversary must reconstruct"
    - "generating dependent random variables over networks"
    - "using a secret key to foil an eavesdropper"

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)