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: "common information revisited"
  Abstract: One of the main notions of information theory is the notion of mutual information in two messages (two random variables in Shannon information theory or two binary strings in algorithmic information theory). The mutual information in $x$ and $y$ measures how much the transmission of $x$ can be simplified if both the sender and the recipient know $y$ in advance. G\'acs and K\"orner gave an example where mutual information cannot be presented as common information (a third message easily extractable from both $x$ and $y$). Then this question was studied in the framework of algorithmic information theory by An. Muchnik and A. Romashchenko who found many other examples of this type. K. Makarychev and Yu. Makarychev found a new proof of G\'acs--K\"orner results by means of conditionally independent random variables. The question about the difference between mutual and common information can be studied quantitatively: for a given $x$ and $y$ we look for three messages $a$, $b$, $c$ such that $a$ and $c$ are enough to reconstruct $x$, while $b$ and $c$ are enough to reconstruct $y$. In this paper: We state and prove (using hypercontractivity of product spaces) a quantitative version of G\'acs--K\"orner theorem; We study the tradeoff between $\abs{a}, \abs{b}, \abs{c}$ for a random pair $(x, y)$ such that Hamming distance between $x$ and $y$ is $\eps n$ (our bounds are almost tight); We construct "the worst possible" distribution on $(x, y)$ in terms of the tradeoff between $\abs{a}, \abs{b}, \abs{c}$.

Connected through hyperedge HE-1 (induced by source paper "an operational characterization of mutual information in algorithmic information theory"):
  with members:
    - "optimal probabilistic polynomial time compression and the slepian wolf theorem tighter version and simple proofs"
    - "kolmogorov complexity version of slepian wolf coding"
    - "conditional information inequalities and combinatorial applications"
    - "linear list approximation for short programs or the power of a few random bits"
    - "conditional information inequalities for entropic and almost entropic points"
    - "common information and secret key capacity"
    - "optimal rate code constructions for computationally simple channels"
    - "variations on muchnik s conditional complexity theorem"

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)