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: "marvels and pitfalls of the langevin algorithm in noisy high dimensional inference"
  Abstract: Gradient-descent-based algorithms and their stochastic versions have widespread applications in machine learning and statistical inference. In this work we perform an analytic study of the performances of one of them, the Langevin algorithm, in the context of noisy high-dimensional inference. We employ the Langevin algorithm to sample the posterior probability measure for the spiked matrix-tensor model. The typical behaviour of this algorithm is described by a system of integro-differential equations that we call the Langevin state evolution, whose solution is compared with the one of the state evolution of approximate message passing (AMP). Our results show that, remarkably, the algorithmic threshold of the Langevin algorithm is sub-optimal with respect to the one given by AMP. We conjecture this phenomenon to be due to the residual glassiness present in that region of parameters. Finally we show how a landscape-annealing protocol, that uses the Langevin algorithm but violate the Bayes-optimality condition, can approach the performance of AMP.

Connected through hyperedge HE-1 (induced by source paper "thresholds of descending algorithms in inference problems"):
  with members:
    - "passed spurious descent algorithms and local minima in spiked matrix tensor models"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "who is afraid of big bad minima analysis of gradient flow in a spiked matrix tensor model"):
  with members:
    - "passed spurious descent algorithms and local minima in spiked matrix tensor models"
    - "on the optimization landscape of tensor decompositions"
    - "depth creates no bad local minima"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "information theoretically optimal sparse pca"
    - "a statistical model for tensor pca"

Connected through hyperedge HE-3 (induced by source paper "passed spurious analysing descent algorithms and local minima in spiked matrix tensor model"):
  with members:
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "depth creates no bad local minima"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "a statistical model for tensor pca"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "passed spurious descent algorithms and local minima in spiked matrix tensor models"):
  with members:
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "depth creates no bad local minima"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"

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.LG(Machine Learning)