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: "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
  Abstract: This paper considers probabilistic estimation of a low-rank matrix from non-linear element-wise measurements of its elements. We derive the corresponding approximate message passing (AMP) algorithm and its state evolution. Relying on non-rigorous but standard assumptions motivated by statistical physics, we characterize the minimum mean squared error (MMSE) achievable information theoretically and with the AMP algorithm. Unlike in related problems of linear estimation, in the present setting the MMSE depends on the output channel only trough a single parameter - its Fisher information. We illustrate this striking finding by analysis of submatrix localization, and of detection of communities hidden in a dense stochastic block model. For this example we locate the computational and statistical boundaries that are not equal for rank larger than four.

Connected through hyperedge HE-1 (induced by source paper "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries"):
  with members:
    - "how hard is robust mean estimation"
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "computational hardness of certifying bounds on constrained pca problems"
    - "efficient algorithms for outlier robust regression"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "robust sparse estimation tasks in high dimensions"
    - "the power of sum of squares for detecting hidden structures"
    - "bayesian estimation from few samples community detection and related problems"

Connected through hyperedge HE-2 (induced by source paper "the kikuchi hierarchy and tensor pca"):
  with members:
    - "the power of sum of squares for detecting hidden structures"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "sum of squares certificates for maxima of random tensors on the sphere"
    - "multiplicative approximations for polynomial optimization over the unit sphere"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "strongly refuting random csps below the spectral threshold"
    - "phase transitions in sparse pca"
    - "how to refute a random csp"

Connected through hyperedge HE-3 (induced by source paper "computational hardness of certifying bounds on constrained pca problems"):
  with members:
    - "mean field approximation convex hierarchies and the optimality of correlation rounding a unified perspective"
    - "heterogeneous multireference alignment a single pass approach"
    - "community detection in hypergraphs spiked tensor models and sum of squares"
    - "the lov asz theta function for random regular graphs and community detection in the hard regime"
    - "the power of sum of squares for detecting hidden structures"
    - "bayesian estimation from few samples community detection and related problems"
    - "sum of squares lower bounds for refuting any csp"
    - "a nearly tight sum of squares lower bound for the planted clique problem"

Connected through hyperedge HE-4 (induced by source paper "message passing algorithms for synchronization problems over compact groups"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "phase transitions in sparse pca"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "information theoretically optimal sparse pca"

Connected through hyperedge HE-5 (induced by source paper "finite sample analysis of approximate message passing algorithms"):
  with members:
    - "analysis of approximate message passing with a class of non separable denoisers"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "information theoretically optimal sparse pca"
    - "non negative principal component analysis message passing algorithms and sharp asymptotics"

Connected through hyperedge HE-6 (induced by source paper "rank one matrix estimation analysis of algorithmic and information theoretic limits by the spatial coupling method"):
  with members:
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "mutual information in rank one matrix estimation"
    - "asymptotic mutual information for the two groups stochastic block model"
    - "information theoretically optimal sparse pca"
    - "a simple proof of maxwell saturation for coupled scalar recursions"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-7 (induced by source paper "on the randomized complexity of minimizing a convex quadratic function"):
  with members:
    - "tight query complexity lower bounds for pca via finite sample deformed wigner law"
    - "estimation in the spiked wigner model a short proof of the replica formula"
    - "optimal lower bounds for universal relation samplers and finding duplicates"
    - "lifting randomized query complexity to randomized communication complexity"
    - "mutual information in rank one matrix estimation"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "lower bounds for adaptive sparse recovery"

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"
    - "a note on the strong data processing inequalities in bayesian networks"
    - "dissipation of information in channels with input constraints"

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)