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 the r e nyi divergence and the joint range of relative entropies"
  Abstract: This paper starts with a study of the minimum of the R\'{e}nyi divergence subject to a fixed (or minimal) value of the total variation distance. Relying on the solution of this minimization problem, we determine the exact region of the points $\bigl( D(Q||P_1), D(Q||P_2) \bigr)$ where $P_1$ and $P_2$ are any probability distributions whose total variation distance is not below a fixed value, and the probability distribution $Q$ is arbitrary (none of these three distributions is assumed to be fixed). It is further shown that all the points of this convex region are attained by a triple of 2-element probability distributions. As a byproduct of this characterization, we provide a geometric interpretation of the minimal Chernoff information subject to a minimal total variation distance. Finally, as a separate issue, a new exponential upper bound on the block error probability of ML decoded binary linear block codes is introduced. Its error exponent depends on the R\'{e}nyi divergence (of orders above 1), used for quantifying the degradation in performance of block codes in terms of the deviation of their distance spectra from the binomial distribution.

Connected through hyperedge HE-1 (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"
    - "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"
    - "concentration of measure inequalities and their communication and information theoretic applications"

Connected through hyperedge HE-2 (induced by source paper "upper bounds on the relative entropy and r enyi divergence as a function of total variation distance for finite alphabets"):
  with members:
    - "optimal quantization for distribution synthesis"
    - "bounds among f divergences"
    - "channels with cost constraints strong converse and dispersion"
    - "renyi divergence and kullback leibler divergence"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "on pinsker s and vajda s type inequalities for csiszar s f divergences"

Connected through hyperedge HE-3 (induced by source paper "on reverse pinsker inequalities"):
  with members:
    - "optimal quantization for distribution synthesis"
    - "channels with cost constraints strong converse and dispersion"
    - "renyi divergence and kullback leibler divergence"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "on pinsker s and vajda s type inequalities for csiszar s f divergences"

Connected through hyperedge HE-4 (induced by source paper "tight bounds for symmetric divergence measures and a new inequality relating f divergences"):
  with members:
    - "tight bounds for symmetric divergence measures and a refined bound for lossless source coding"
    - "capacity of diffusion based molecular communication networks over lti poisson channels"
    - "on pinsker s and vajda s type inequalities for csiszar s f divergences"

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)