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: "dissipation of information in channels with input constraints"
  Abstract: One of the basic tenets in information theory, the data processing inequality states that output divergence does not exceed the input divergence for any channel. For channels without input constraints, various estimates on the amount of such contraction are known, Dobrushin's coefficient for the total variation being perhaps the most well-known. This work investigates channels with average input cost constraint. It is found that while the contraction coefficient typically equals one (no contraction), the information nevertheless dissipates. A certain non-linear function, the \emph{Dobrushin curve} of the channel, is proposed to quantify the amount of dissipation. Tools for evaluating the Dobrushin curve of additive-noise channels are developed based on coupling arguments. Some basic applications in stochastic control, uniqueness of Gibbs measures and fundamental limits of noisy circuits are discussed. #R##N#As an application, it shown that in the chain of $n$ power-constrained relays and Gaussian channels the end-to-end mutual information and maximal squared correlation decay as $\Theta(\frac{\log\log n}{\log n})$, which is in stark contrast with the exponential decay in chains of discrete channels. Similarly, the behavior of noisy circuits (composed of gates with bounded fan-in) and broadcasting of information on trees (of bounded degree) does not experience threshold behavior in the signal-to-noise ratio (SNR). Namely, unlike the case of discrete channels, the probability of bit error stays bounded away from $1\over 2$ regardless of the SNR.

Connected through hyperedge HE-1 (induced by source paper "broadcasting on random directed acyclic graphs"):
  with members:
    - "reliable computation by formulas of noisy and gates and noiseless xor gates with applications to quantum mechanics"
    - "noise thresholds for amplification quantum foundations from classical fault tolerant computation"
    - "strong data processing inequalities for channels and bayesian networks"
    - "reconstruction and clustering in random constraint satisfaction problems"
    - "noise threshold for universality of 2 input gates"

Connected through hyperedge HE-2 (induced by source paper "accuracy memory tradeoffs and phase transitions in belief propagation"):
  with members:
    - "the circuit complexity of inference"
    - "communication complexity of estimating correlations"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "extractor based time space lower bounds for learning"
    - "secret key generation with limited interaction"
    - "fast learning requires good memory a time space lower bound for parity learning"

Connected through hyperedge HE-3 (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-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 "coded iterative computing using substitute decoding"):
  with members:
    - "fundamental limits of coded linear transform"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "near optimal coded data shuffling for distributed learning"
    - "coded sparse matrix multiplication"
    - "straggler mitigation by delayed relaunch of tasks"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "short dot computing large linear transforms distributedly using coded short dot products"

Connected through hyperedge HE-6 (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"
    - "renyi divergence and kullback leibler divergence"
    - "on pinsker s and vajda s type inequalities for csiszar s f divergences"

Connected through hyperedge HE-7 (induced by source paper "comparison of channels criteria for domination by a symmetric channel"):
  with members:
    - "strong data processing inequalities for channels and bayesian networks"
    - "bounds between contraction coefficients"
    - "a note on the strong data processing inequalities in bayesian networks"
    - "universal polar codes for more capable and less noisy channels and sources"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "capacity regions of two new classes of 2 receiver broadcast channels"

Connected through hyperedge HE-8 (induced by source paper "application of information percolation method to reconstruction problems on graphs"):
  with members:
    - "comparison of channels criteria for domination by a symmetric channel"
    - "strong data processing inequalities for channels and bayesian networks"
    - "mutual information in rank one matrix estimation"
    - "asymptotic mutual information for the two groups stochastic block model"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "a note on the strong data processing inequalities in bayesian networks"

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)