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: "fano type inequalities based on general conditional information measures over countably infinite alphabets with list decoding"
  Abstract: Fano's inequality is one of the most elementary, ubiquitous and important tools in information theory. This study generalizes Fano's inequality in the following four ways: (i) the alphabet $\mathcal{X}$ of the random variable $X$ to be estimated is \emph{countably infinite;} (ii) the probability distribution $P_{X}$ of $X$ is fixed to a given discrete probability distribution $Q$; (iii) the inequality is established for a general conditional information measure $\mathfrak{h}_{\phi}(X \mid Y)$; and (iv) the decoding rule is a list decoding scheme, in contrast to a unique decoding scheme. In other words, our main results concern tight upper bounds on $\mathfrak{h}_{\phi}(X \mid Y)$ subject to an admissible list decoding error probability and a fixed $\mathcal{X}$-marginal $P_{X} = Q$. Since $\mathfrak{h}_{\phi}(X \mid Y)$ admits the conditional Shannon and R\'{e}nyi's entropies as special cases, our Fano-type inequalities subsumes known generalizations of Fano's inequality. Moreover, since $\mathfrak{h}_{\phi}(X \mid Y)$ is a general definition without explicit form of a function $\phi$, our Fano-type inequalities also provide some insights on how to measure conditional information. As an application of our Fano-type inequalities, we investigate various asymptotic estimates on the equivocations under the condition that the error probability vanishes. Such asymptotic estimates are important consequences of Fano's inequality. Most interestingly, a consequence of our results is a novel characterization of the asymptotic equipartition property (AEP).

Connected through hyperedge HE-1 (induced by source paper "countably infinite multilevel source polarization for non stationary erasure distributions"):
  with members:
    - "asymptotic distribution of multilevel channel polarization for a certain class of erasure channels"
    - "on the polarization levels of automorphic symmetric channels"
    - "general strong polarization"
    - "modular arithmetic erasure channels and their multilevel channel polarization"
    - "fourier analysis of mac polarization"
    - "a generalized erasure channel in the sense of polarization for binary erasure channels"
    - "polar coding for non stationary channels"
    - "entropy bounds on abelian groups and the ruzsa divergence"

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)