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: "provable learning of noisy or networks"
  Abstract: Many machine learning applications use latent variable models to explain structure in data, whereby visible variables (= coordinates of the given datapoint) are explained as a probabilistic function of some hidden variables. Finding parameters with the maximum likelihood is NP-hard even in very simple settings. In recent years, provably efficient algorithms were nevertheless developed for models with linear structures: topic models, mixture models, hidden markov models, etc. These algorithms use matrix or tensor decomposition, and make some reasonable assumptions about the parameters of the underlying model. #R##N#But matrix or tensor decomposition seems of little use when the latent variable model has nonlinearities. The current paper shows how to make progress: tensor decomposition is applied for learning the single-layer {\em noisy or} network, which is a textbook example of a Bayes net, and used for example in the classic QMR-DT software for diagnosing which disease(s) a patient may have by observing the symptoms he/she exhibits. #R##N#The technical novelty here, which should be useful in other settings in future, is analysis of tensor decomposition in presence of systematic error (i.e., where the noise/error is correlated with the signal, and doesn't decrease as number of samples goes to infinity). This requires rethinking all steps of tensor decomposition methods from the ground up. #R##N#For simplicity our analysis is stated assuming that the network parameters were chosen from a probability distribution but the method seems more generally applicable.

Connected through hyperedge HE-1 (induced by source paper "on the convergence rate of training recurrent neural networks"):
  with members:
    - "can sgd learn recurrent neural networks with provable generalization"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "robust spectral filtering and anomaly detection"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "learning long term dependencies via fourier recurrent units"

Connected through hyperedge HE-2 (induced by source paper "polynomial time probabilistic reasoning with partial observations via implicit learning in probability logics"):
  with members:
    - "bayesian estimation from few samples community detection and related problems"
    - "tensor principal component analysis via sum of squares proofs"
    - "rounding sum of squares relaxations"
    - "hypercontractivity sum of squares proofs and their applications"
    - "from statistical knowledge bases to degrees of belief"
    - "characterizing and reasoning about probabilistic and non probabilistic expectation"

Connected through hyperedge HE-3 (induced by source paper "sgd learns the conjugate kernel class of the network"):
  with members:
    - "random features for compositional kernels"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "convexified convolutional neural networks"
    - "learning halfspaces and neural networks with random initialization"
    - "deep convolutional networks are hierarchical kernel machines"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "ell_1 regularized neural networks are improperly learnable in polynomial time"
    - "steps toward deep kernel methods from infinite neural networks"

Connected through hyperedge HE-4 (induced by source paper "recovery guarantees for one hidden layer neural networks"):
  with members:
    - "relative error tensor low rank approximation"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "deep online convex optimization with gated games"
    - "neural taylor approximations convergence and exploration in rectifier networks"
    - "reliably learning the relu in polynomial time"
    - "distribution specific hardness of learning neural networks"
    - "low rank approximation with entrywise ell_1 norm error"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-5 (induced by source paper "beyond log concavity provable guarantees for sampling multi modal distributions using simulated tempering langevin monte carlo"):
  with members:
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "simple efficient and neural algorithms for sparse coding"
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"
    - "on some provably correct cases of variational inference for topic models"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a spectral algorithm for latent dirichlet allocation"
    - "learning topic models going beyond svd"

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)