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: "a vest of the pseudoinverse learning algorithm"
  Abstract: In this paper, we briefly review the basic scheme of the pseudoinverse learning (PIL) algorithm and present some discussions on the PIL, as well as its variants. The PIL algorithm, first presented in 1995, is a non-gradient descent and non-iterative learning algorithm for multi-layer neural networks and has several advantages compared with gradient descent based algorithms. Some new viewpoints to PIL algorithm are presented, and several common pitfalls in practical implementation of the neural network learning task are also addressed. In addition, we show that so called extreme learning machine is a Variant crEated by Simple name alTernation (VEST) of the PIL algorithm for single hidden layer feedforward neural networks.

Connected through hyperedge HE-1 (induced by source paper "stacked autoencoder based deep random vector functional link neural network for classification"):
  with members:
    - "random vector functional link neural network based ensemble deep learning"
    - "multi layered gradient boosting decision trees"
    - "enhancing multi class classification of random forest using random vector functional neural network and oblique decision surfaces"
    - "self normalizing neural networks"
    - "densely connected convolutional networks"
    - "training neural networks without gradients a scalable admm approach"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "random vector functional link neural network based ensemble deep learning"):
  with members:
    - "enhancing multi class classification of random forest using random vector functional neural network and oblique decision surfaces"
    - "snapshot ensembles train 1 get m for free"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "deep learning in neural networks"

Connected through hyperedge HE-3 (induced by source paper "on improving learning capability of elm and an application to brain computer interface"):
  with members:
    - "a study on effectiveness of extreme learning machine"

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)