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: "threshold effects in parameter estimation from compressed data"
  Abstract: In this paper, we investigate threshold effects associated with the swapping of signal and noise subspaces in estimating signal parameters from compressed noisy data. The term threshold effect refers to a sharp departure of mean-squared error from the Cramer–Rao bound when the signal-to-noise ratio falls below a threshold SNR. In many cases, the threshold effect is caused by a subspace swap event, when the measured data (or its sample covariance) is better approximated by a subset of components of an orthogonal subspace than by the components of a signal subspace. We derive analytical lower bounds on the probability of a subspace swap in compressively measured noisy data in two canonical models: a first-order model and a second-order model. In the first-order model, the parameters to be estimated modulate the mean of a complex multivariate normal set of measurements. In the second-order model, the parameters modulate the covariance of complex multivariate measurements. In both cases, the probability bounds are tail probabilities of   $F$  -distributions, and they apply to any linear compression scheme. These lower bounds guide our understanding of threshold effects and performance breakdowns for parameter estimation using compression. In particular, they can be used to quantify the increase in threshold SNR as a function of a compression ratio   $C$   . We demonstrate numerically that this increase in threshold SNR is roughly   $10\log_{10} \ C$   dB, which is consistent with the performance loss that one would expect when measurements in Gaussian noise are compressed by a factor   $C$   .

Connected through hyperedge HE-1 (induced by source paper "minimax optimal online stochastic learning for sequences of convex functions under sub gradient observation failures"):
  with members:
    - "introduction to online convex optimization"
    - "bandit convex optimization with biased noisy gradient oracles"
    - "scale free online learning"
    - "online learning with feedback graphs beyond bandits"
    - "minimax analysis of active learning"
    - "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"
    - "online and batch learning algorithms for data with missing features"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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)