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 "strong data processing inequalities for input constrained additive noise channels"):
  Abstract: This paper quantifies the intuitive observation that adding noise reduces available information by means of non-linear strong data processing inequalities. Consider the random variables $W\to X\to Y$ forming a Markov chain, where $Y=X+Z$ with $X$ and $Z$ real-valued, independent and $X$ bounded in $L_p$-norm. It is shown that $I(W;Y) \le F_I(I(W;X))$ with $F_I(t) 0$, if and only if $Z$ has a density whose support is not disjoint from any translate of itself. A related question is to characterize for what couplings $(W,X)$ the mutual information $I(W;Y)$ is close to maximum possible. To that end we show that in order to saturate the channel, i.e. for $I(W;Y)$ to approach capacity, it is mandatory that $I(W;X)\to\infty$ (under suitable conditions on the channel). A key ingredient for this result is a deconvolution lemma which shows that post-convolution total variation distance bounds the pre-convolution Kolmogorov-Smirnov distance. Explicit bounds are provided for the special case of the additive Gaussian noise channel with quadratic cost constraint. These bounds are shown to be order-optimal. For this case simplified proofs are provided leveraging Gaussian-specific tools such as the connection between information and estimation (I-MMSE) and Talagrand's information-transportation inequality.

Includes members:
  Member "dissipation of information in channels with input constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "on data processing and majorization inequalities for f divergences with applications")
  Member "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "estimation in gaussian noise properties of the minimum mean square error":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressed coding amp based decoding and analog spatial coupling")
  Member "on pairs of f divergences and their joint range":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a better bound gives a hundred rounds enhanced privacy guarantees via f divergences")
  Member "mutual information and minimum mean square error in gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")

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)