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: "e_ gamma resolvability"
  Abstract: The conventional channel resolvability refers to the minimum rate needed for an input process to approximate the channel output distribution in total variation distance. In this paper we study $E_{\gamma}$-resolvability, in which total variation is replaced by the more general $E_{\gamma}$ distance. A general one-shot achievability bound for the precision of such an approximation is developed. Let $Q_{\sf X|U}$ be a random transformation, $n$ be an integer, and $E\in(0,+\infty)$. We show that in the asymptotic setting where $\gamma=\exp(nE)$, a (nonnegative) randomness rate above $\inf_{Q_{\sf U}: D(Q_{\sf X}\|{{\pi}}_{\sf X})\le E} \{D(Q_{\sf X}\|{{\pi}}_{\sf X})+I(Q_{\sf U},Q_{\sf X|U})-E\}$ is sufficient to approximate the output distribution ${{\pi}}_{\sf X}^{\otimes n}$ using the channel $Q_{\sf X|U}^{\otimes n}$, where $Q_{\sf U}\to Q_{\sf X|U}\to Q_{\sf X}$, and is also necessary in the case of finite $\mathcal{U}$ and $\mathcal{X}$. In particular, a randomness rate of $\inf_{Q_{\sf U}}I(Q_{\sf U},Q_{\sf X|U})-E$ is always sufficient. We also study the convergence of the approximation error under the high probability criteria in the case of random codebooks. Moreover, by developing simple bounds relating $E_{\gamma}$ and other distance measures, we are able to determine the exact linear growth rate of the approximation errors measured in relative entropy and smooth R\'{e}nyi divergences for a fixed-input randomness rate. The new resolvability result is then used to derive 1) a one-shot upper bound on the probability of excess distortion in lossy compression, which is exponentially tight in the i.i.d.~setting, 2) a one-shot version of the mutual covering lemma, and 3) a lower bound on the size of the eavesdropper list to include the actual message and a lower bound on the eavesdropper false-alarm probability in the wiretap channel problem, which is (asymptotically) ensemble-tight.

Connected through hyperedge HE-1 (induced by source paper "on data processing and majorization inequalities for f divergences with applications"):
  with members:
    - "minimum entropy couplings and their applications"
    - "on f divergences integral representations local behavior and inequalities"
    - "comparison of channels criteria for domination by a symmetric channel"
    - "strong data processing inequalities for channels and bayesian networks"
    - "bounds on the entropy of a function of a random variable and their applications"
    - "f divergence inequalities"
    - "bounds between contraction coefficients"
    - "strong data processing inequalities for input constrained additive noise channels"

Connected through hyperedge HE-2 (induced by source paper "on privacy amplification lossy compression and their duality to channel coding"):
  with members:
    - "wiretap channels nonasymptotic fundamental limits"
    - "uniform random number generation from markov chains non asymptotic and asymptotic analyses"
    - "alignment of polarized sets"
    - "approaching the rate distortion limit with spatial coupling belief propagation and decimation"
    - "non asymptotic output statistics of random binning and its applications"
    - "tight exponential analysis of universally composable privacy amplification and its applications"
    - "non asymptotic analysis of privacy amplification via renyi entropy and inf spectral entropy"
    - "fixed length lossy compression in the finite blocklength regime"

Connected through hyperedge HE-3 (induced by source paper "on f divergences integral representations local behavior and inequalities"):
  with members:
    - "a note on reverse pinsker inequalities"
    - "f divergence inequalities"
    - "f divergence inequalities via functional domination"
    - "on the renyi divergence joint range of relative entropies and a channel coding theorem"
    - "tight bounds for symmetric divergence measures and a refined bound for lossless source coding"
    - "dissipation of information in channels with input constraints"
    - "renyi divergence and kullback leibler divergence"
    - "on pinsker s and vajda s type inequalities for csiszar s f divergences"

Connected through hyperedge HE-4 (induced by source paper "f divergence inequalities"):
  with members:
    - "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach"
    - "optimal quantization for distribution synthesis"
    - "bounds between contraction coefficients"
    - "one shot mutual covering lemma and marton s inner bound with a common message"
    - "refinement of some moment inequalities"
    - "on the renyi divergence joint range of relative entropies and a channel coding theorem"
    - "resolvability in e γ with applications to lossy compression and wiretap channels"
    - "tight bounds for symmetric divergence measures and a refined bound for lossless source coding"

Connected through hyperedge HE-5 (induced by source paper "finite blocklength bounds for wiretap channels"):
  with members:
    - "security analysis of varepsilon almost dual universal 2 hash functions smoothing of min entropy versus smoothing of renyi entropy of order 2"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "the third order term in the normal approximation for the awgn channel"
    - "on the secrecy exponent of the wire tap channel"
    - "converses for secret key agreement and secure computing"
    - "information spectrum approach to strong converse theorems for degraded wiretap channels"
    - "strong converse for a degraded wiretap channel via active hypothesis testing"
    - "bayesian m ary hypothesis testing the meta converse and verd u han bounds are tight"

Connected through hyperedge HE-6 (induced by source paper "secret key generation with limited interaction"):
  with members:
    - "strong data processing inequalities for channels and bayesian networks"
    - "the likelihood encoder for lossy compression"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "converses for secret key agreement and secure computing"
    - "secret key agreement general capacity and second order asymptotics"
    - "key capacity for product sources with application to stationary gaussian processes"
    - "outer bounds for multiterminal source coding via a strong data processing inequality"
    - "common information and secret key capacity"

Connected through hyperedge HE-7 (induced by source paper "smoothing brascamp lieb inequalities and strong converses for common randomness generation"):
  with members:
    - "brascamp lieb inequality and its reverse an information theoretic view"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "one shot mutual covering lemma and marton s inner bound with a common message"
    - "how many queries will resolve common randomness"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "fixed length lossy compression in the finite blocklength regime"
    - "information spectrum approach to second order coding rate in channel coding"

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)