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: "robust spectral filtering and anomaly detection"
  Abstract: We consider a setting, where the output of a linear dynamical system (LDS) is, with an unknown but fixed probability, replaced by noise. There, we present a robust method for the prediction of the outputs of the LDS and identification of the samples of noise, and prove guarantees on its statistical performance. One application lies in anomaly detection: the samples of noise, unlikely to have been generated by the dynamics, can be flagged to operators of the system for further study.

Connected through hyperedge HE-1 (induced by source paper "learning in gated neural networks"):
  with members:
    - "can sgd learn recurrent neural networks with provable generalization"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "towards binary valued gates for robust lstm training"
    - "learning one hidden layer neural networks under general input distributions"
    - "breaking the gridlock in mixture of experts consistent and efficient algorithms"
    - "on the convergence rate of training recurrent neural networks"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "learning one hidden layer neural networks with landscape design"

Connected through hyperedge HE-2 (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"
    - "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"
    - "spectral filtering for general linear dynamical systems"

Connected through hyperedge HE-3 (induced by source paper "a convergence theory for deep learning via over parameterization"):
  with members:
    - "non asymptotic identification of lti systems from a single trajectory"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "learning compact neural networks with regularization"
    - "on the convergence rate of training recurrent neural networks"
    - "learning long term dependencies via fourier recurrent units"
    - "spectral filtering for general linear dynamical systems"

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)