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 pairs of f divergences and their joint range"
  Abstract: We compare two f-divergences and prove that their joint range is the convex hull of the joint range for distributions supported on only two points. Some applications of this result are given.

Connected through hyperedge HE-1 (induced by source paper "a better bound gives a hundred rounds enhanced privacy guarantees via f divergences"):
  with members:
    - "hypothesis testing interpretations and renyi differential privacy"
    - "privacy amplification by mixing and diffusion mechanisms"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "privacy amplification by iteration"
    - "renyi differential privacy"
    - "f divergence inequalities"

Connected through hyperedge HE-2 (induced by source paper "gaussian differential privacy"):
  with members:
    - "bag of freebies for training object detection neural networks"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "bag of tricks for image classification with convolutional neural networks"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "renyi differential privacy"
    - "collecting telemetry data privately"
    - "concentrated differential privacy simplifications extensions and lower bounds"

Connected through hyperedge HE-3 (induced by source paper "bregman divergence bounds and universality properties of the logarithmic loss"):
  with members:
    - "on f divergences integral representations local behavior and inequalities"
    - "on the universality of the logistic loss function"
    - "universality of logarithmic loss in lossy compression"
    - "opening the black box of deep neural networks via information"
    - "f divergence inequalities"
    - "generalized independent component analysis over finite alphabets"
    - "justification of logarithmic loss via the benefit of side information"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-4 (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-5 (induced by source paper "strong data processing inequalities for channels and bayesian networks"):
  with members:
    - "novel lower bounds on the entropy rate of binary hidden markov processes"
    - "converses for distributed estimation via strong data processing inequalities"
    - "on the entropy of a noisy function"
    - "strong data processing inequalities for input constrained additive noise channels"
    - "dissipation of information in channels with input constraints"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "tractable inference for complex stochastic processes"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"

Connected through hyperedge HE-6 (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"
    - "e_ gamma resolvability"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "the quality of the covariance selection through detection problem and auc bounds"):
  with members:
    - "latent tree approximation in linear model"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "model selection through sparse maximum likelihood estimation"

Connected through hyperedge HE-8 (induced by source paper "bounds among f divergences"):
  with members:
    - "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach"
    - "f divergence inequalities"
    - "optimal quantization for distribution synthesis"
    - "on the r e nyi divergence and the joint range of relative entropies"
    - "one shot mutual covering lemma and marton s inner bound with a common message"
    - "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"

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)