human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a note on the strong data processing inequalities in bayesian networks"):
  Abstract: The data-processing inequality, that is, $I(U;Y) \le I(U;X)$ for a Markov chain $U \to X \to Y$, has been the method of choice for proving impossibility (converse) results in information theory and many other disciplines. Various channel-dependent strengthenings of this inequality have been proposed both classically and more recently. This note considers the basic information-theoretic question: given strong data-processing inequality (SDPI) for each constituent channel in a Bayesian network, how does one produce an end-to-end SDPI? #R##N#Our approach is based on the (extract of the) Evans-Schulman method, which is demonstrated for three different kinds of SDPIs, namely, the usual Ahslwede-G\'acs type contraction coefficients (mutual information), Dobrushin's contraction coefficients (total variation), and finally the $F_I$-curve (the best possible SDPI for a given channel). As an example, it is shown how to obtain SDPI for an $n$-letter memoryless channel with feedback given an SDPI for $n=1$.

Includes members:
  Member "novel lower bounds on the entropy rate of binary hidden markov processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "computable lower bounds for capacities of input driven finite state channels")
  Member "converses for distributed estimation via strong data processing inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "information theoretic perspectives on brascamp lieb inequality and its reverse")
  Member "bounds between contraction coefficients":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on universal features for high dimensional learning and inference")
  Member "on the entropy of a noisy function":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "guessing with a bit of help")
  Member "secret key generation with one communicator and a one shot converse via hypercontractivity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication for generating correlation a unifying survey")
  Member "strong data processing inequalities for input constrained additive noise channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on data processing and majorization inequalities for f divergences with applications")
  Member "dissipation of information in channels with input constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "broadcasting on random directed acyclic graphs")
  Member "strong data processing inequalities and phi sobolev inequalities for discrete channels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on data processing and majorization inequalities for f divergences with applications")

Question: Please tell me which class the source paper 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)