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: "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.

Connected through hyperedge HE-1 (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-2 (induced by source paper "estimation in poisson noise properties of the conditional mean estimator"):
  with members:
    - "mutual information relative entropy and estimation error in semi martingale channels"
    - "a de bruijn identity for discrete random variables"
    - "bounds on the lambert function and their application to the outage analysis of user cooperation"
    - "on regret of parametric mismatch in minimum mean square error estimation"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "compressed sensing performance bounds under poisson noise"
    - "mutual information relative entropy and estimation in the poisson channel"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "on the robustness of information theoretic privacy measures and mechanisms"):
  with members:
    - "data disclosure under perfect sample privacy"
    - "privacy against brute force inference attacks"
    - "estimation efficiency under privacy constraints"
    - "data privacy for a rho recoverable function"
    - "an operational approach to information leakage"
    - "the utility cost of robust privacy guarantees"
    - "privacy under hard distortion constraints"
    - "a tunable measure for information leakage"

Connected through hyperedge HE-4 (induced by source paper "privacy with estimation guarantees"):
  with members:
    - "the utility cost of robust privacy guarantees"
    - "generalizing bottleneck problems"
    - "matching anonymized and obfuscated time series to users profiles"
    - "privacy preserving adversarial networks"
    - "renyi differential privacy"
    - "principal inertia components and applications"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "an estimation theoretic view of privacy"

Connected through hyperedge HE-5 (induced by source paper "the utility cost of robust privacy guarantees"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "an estimation theoretic view of privacy"
    - "f divergence inequalities"
    - "information extraction under privacy constraints"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "privacy against statistical inference"

Connected through hyperedge HE-6 (induced by source paper "an estimation theoretic view of privacy"):
  with members:
    - "the utility cost of robust privacy guarantees"
    - "generalizing bottleneck problems"
    - "matching anonymized and obfuscated time series to users profiles"
    - "privacy preserving adversarial networks"
    - "context aware generative adversarial privacy"
    - "principal inertia components and applications"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "privacy aware mmse estimation"

Connected through hyperedge HE-7 (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"
    - "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"
    - "on pairs of f divergences and their joint range"

Connected through hyperedge HE-8 (induced by source paper "bounds between contraction coefficients"):
  with members:
    - "comparison of channels criteria for domination by a symmetric channel"
    - "f divergence inequalities"
    - "information extraction under privacy constraints"
    - "tight bounds for symmetric divergence measures and a new inequality relating f divergences"
    - "dissipation of information in channels with input constraints"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "linear information coupling problems"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"

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)