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: "bounds on the lambert function and their application to the outage analysis of user cooperation"
  Abstract: Problems formulated in terms of logarithmic or exponential equations often use the Lambert W function in their solutions. Expansions, approximations and bounds on W have been derived in an effort to gain a better understanding of the relationship between equation parameters. In this paper, we focus on one of the branches of W, denoted as W-1, we derive tractable upper and lower bounds and we illustrate their usefulness in identifying conditions under which user cooperation can yield a lower outage probability than non-cooperative transmission.

Connected through hyperedge HE-1 (induced by source paper "thompson sampling for combinatorial network optimization in unknown environments"):
  with members:
    - "batch size independent regret bounds for the combinatorial multi armed bandit problem"
    - "stochastic online learning with probabilistic graph feedback"
    - "combinatorial bandits with full bandit feedback sample complexity and regret minimization"
    - "thompson sampling for combinatorial semi bandits"
    - "bandits with side observations bounded vs logarithmic regret"
    - "analysis of thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "regret bounds for stochastic combinatorial multi armed bandits with linear space complexity"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"

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"
    - "strong data processing inequalities for input constrained additive noise channels"
    - "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 "bandits with side observations bounded vs logarithmic regret"):
  with members:
    - "regret analysis of the anytime optimally confident ucb algorithm"
    - "online learning with feedback graphs beyond bandits"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "leveraging side observations in stochastic bandits"
    - "from bandits to experts on the value of side observations"

Connected through hyperedge HE-4 (induced by source paper "feature selection as monte carlo search in growing single rooted directed acyclic graph by best leaf identification"):
  with members:
    - "structured best arm identification with fixed confidence"
    - "bandit algorithms for tree search"

Connected through hyperedge HE-5 (induced by source paper "pain free random differential privacy with sensitivity sampling"):
  with members:
    - "on the differential privacy of bayesian inference"
    - "the bernstein mechanism function release under differential privacy"
    - "differential privacy for relational algebra improving the sensitivity bounds via constraint systems"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private empirical risk minimization"
    - "learning in a large function space privacy preserving mechanisms for svm learning"

Connected through hyperedge HE-6 (induced by source paper "backscatter multiplicative multiple access systems fundamental limits and practical design"):
  with members:
    - "cooperative ambient backscatter communications for green internet of things"
    - "backscatter communications for internet of things theory and applications"
    - "modulation in the air backscatter communication over ambient ofdm carrier"
    - "full duplex backscatter interference networks based on time hopping spread spectrum"
    - "massive machine type communications in 5g physical and mac layer solutions"

Connected through hyperedge HE-7 (induced by source paper "submodular stochastic probing on matroids"):
  with members:
    - "a stochastic probing problem with applications"
    - "matroid prophet inequalities"
    - "adaptive submodular optimization under matroid constraints"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "mechanism design via correlation gap"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)