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: "on pinsker s and vajda s type inequalities for csiszar s f divergences"
  Abstract: Let D and V denote respectively Information Divergence and Total Variation Distance. Pinsker's and Vajda's inequalities are respectively D ≥ [ 1/ 2] V2 and D ≥ log[( 2+V)/( 2-V)] - [( 2V)/( 2+V)]. In this paper, several generalizations and improvements of these inequalities are established for wide classes of  f -divergences. First, conditions on f are determined under which an f-divergence Df will satisfy Df ≥ cf V2 or Df ≥ c2,f V2 + c4,f V4, where the constants cf, c2,f and c4,f are best possible. As a consequence, lower bounds in terms of V are obtained for many well known distance and divergence measures, including the χ2 and Hellinger's discrimination and the families of Tsallis' and Renyi's divergences. For instance, if D(α) (P||Q) = [α(α-1)]-1 [∫pαq1-αdμ-1] and ℑα (P||Q) = (α-1)-1 log[∫pαq1-αdμ] are respectively the relative information of type α and the Renyi's information gain of order α, it is shown that D(α) ≥ [ 1/ 2] V2 + [ 1/ 72] (α+1)(2-α) V4 whenever -1 ≤ α ≤ 2, α ≠ 0,1 and that ℑα ≥ [( α)/ 2] V2 + [ 1/ 36] α(1 + 5 α- 5 α2 ) V4 for 0 <; α <; 1. In a somewhat different direction, and motivated by the fact that these Pinsker's type lower bounds are accurate only for small variation (V close to zero), lower bounds for Df which are accurate for both small and large variation (V close to two) are also obtained. In the special case of the information divergence they imply that D ≥ log[ 2/( 2-V)] - [( 2-V)/2] log[( 2+V)/2], which uniformly improves Vajda's inequality.

Connected through hyperedge HE-1 (induced by source paper "theoretical evidence for adversarial robustness through randomization"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "on evaluating adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial vulnerability for any classifier"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "the renyi capacity and center"):
  with members:
    - "a strong converse bound for multiple hypothesis testing with applications to high dimensional estimation"
    - "the sphere packing bound for memoryless channels"
    - "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions"
    - "the sphere packing bound for dspcs with feedback a la augustin"
    - "the augustin capacity and center"
    - "the augustin center and the sphere packing bound for memoryless channels"
    - "analysis of remaining uncertainties and exponents under various conditional r e nyi entropies"
    - "on renyi entropy power inequalities"

Connected through hyperedge HE-3 (induced by source paper "theoretical evidence for adversarial robustness through randomization the case of the exponential family"):
  with members:
    - "on evaluating adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "magnet a two pronged defense against adversarial examples"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "countering adversarial images using input transformations"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-4 (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"
    - "e_ gamma resolvability"
    - "bounds between contraction coefficients"

Connected through hyperedge HE-5 (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"
    - "e_ gamma resolvability"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "the augustin capacity and center"):
  with members:
    - "the renyi capacity and center"
    - "the sphere packing bound for memoryless channels"
    - "the augustin center and the sphere packing bound for memoryless channels"
    - "exponent function for stationary memoryless channels with input cost at rates above the capacity"
    - "the sphere packing bound via augustin s method"
    - "channels with cost constraints strong converse and dispersion"
    - "constant compositions in the sphere packing bound for classical quantum channels"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-7 (induced by source paper "the sphere packing bound for dspcs with feedback a la augustin"):
  with members:
    - "the renyi capacity and center"
    - "the sphere packing bound for memoryless channels"
    - "the augustin capacity and center"
    - "the sphere packing bound via augustin s method"
    - "gallager s exponent for mimo channels a reliability rate tradeoff"

Connected through hyperedge HE-8 (induced by source paper "amplifying inter message distance on information divergence measures in big data"):
  with members:
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "message importance measure and its application to minority subset detection in big data"
    - "learning the information divergence"
    - "renyi divergence and kullback leibler 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)