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: "estimating random variables from random sparse observations"
  Abstract: Let X_1,...., X_n be a collection of iid discrete random variables, and Y_1,..., Y_m a set of noisy observations of such variables. Assume each observation Y_a to be a random function of some a random subset of the X_i's, and consider the conditional distribution of X_i given the observations, namely \mu_i(x_i)\equiv\prob\{X_i=x_i|Y\} (a posteriori probability). #R##N#We establish a general relation between the distribution of \mu_i, and the fixed points of the associated density evolution operator. Such relation holds asymptotically in the large system limit, provided the average number of variables an observation depends on is bounded. We discuss the relevance of our result to a number of applications, ranging from sparse graph codes, to multi-user detection, to group testing.

Connected through hyperedge HE-1 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models"):
  with members:
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "information theoretic thresholds from the cavity method"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "the layered structure of tensor estimation and its mutual information"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "opening the black box of deep neural networks via information"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"

Connected through hyperedge HE-2 (induced by source paper "overlap matrix concentration in optimal bayesian inference"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "mutual information for low rank even order symmetric tensor factorization"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "rank one matrix estimation analysis of algorithmic and information theoretic limits by the spatial coupling method"
    - "the mutual information in random linear estimation beyond i i d matrices"
    - "adaptive path interpolation for sparse systems application to a simple censored block model"

Connected through hyperedge HE-3 (induced by source paper "concentration of the matrix valued minimum mean square error in optimal bayesian inference"):
  with members:
    - "overlap matrix concentration in optimal bayesian inference"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "mutual information for low rank even order symmetric tensor factorization"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "rank one matrix estimation analysis of algorithmic and information theoretic limits by the spatial coupling method"
    - "the mutual information in random linear estimation beyond i i d matrices"

Connected through hyperedge HE-4 (induced by source paper "information theoretic thresholds from the cavity method"):
  with members:
    - "planting colourings silently"
    - "mutual information in rank one matrix estimation"
    - "the condensation phase transition in random graph coloring"
    - "asymptotic mutual information for the two groups stochastic block model"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "conditioning and covariance on caterpillars"
    - "chasing the k colorability threshold"
    - "on the complexity of random satisfiability problems with planted solutions"

Connected through hyperedge HE-5 (induced by source paper "marvels and pitfalls of the langevin algorithm in noisy high dimensional inference"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "information theoretic thresholds from the cavity method"
    - "the stochastic interpolation method a simple scheme to prove replica formulas in bayesian inference"
    - "bayesian estimation from few samples community detection and related problems"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "information theoretically optimal sparse pca"
    - "a statistical model for tensor pca"

Connected through hyperedge HE-6 (induced by source paper "phase transitions optimal errors and optimality of message passing in generalized linear models"):
  with members:
    - "the layered structure of tensor estimation and its mutual information"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "statistical physics and approximate message passing algorithms for sparse linear estimation problems in signal processing and coding theory"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "spectral redemption in clustering sparse networks"
    - "expectation maximization gaussian mixture approximate message passing"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-7 (induced by source paper "phase transitions and sample complexity in bayes optimal matrix factorization"):
  with members:
    - "an overview of low rank matrix recovery from incomplete observations"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "information theoretically optimal sparse pca"
    - "bilinear generalized approximate message passing part i derivation"
    - "variational free energies for compressed sensing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "phase diagram and approximate message passing for blind calibration and dictionary learning"

Connected through hyperedge HE-8 (induced by source paper "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"):
  with members:
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "vector precoding for gaussian mimo broadcast channels impact of replica symmetry breaking"
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "graphical models concepts in compressed sensing"
    - "information theoretic bounds for compressed sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"

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)