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: "abstract learning via demodulation in a deep neural network"
  Abstract: Inspired by the brain, deep neural networks (DNN) are thought to learn abstract representations through their hierarchical architecture. However, at present, how this happens is not well understood. Here, we demonstrate that DNN learn abstract representations by a process of demodulation. We introduce a biased sigmoid activation function and use it to show that DNN learn and perform better when optimized for demodulation. Our findings constitute the first unambiguous evidence that DNN perform abstract learning in practical use. Our findings may also explain abstract learning in the human brain.

Connected through hyperedge HE-1 (induced by source paper "instant learning parallel deep neural networks and convolutional bootstrapping"):
  with members:
    - "over sampling in a deep neural network"
    - "the loss surfaces of multilayer networks"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "deep big simple neural nets for handwritten digit recognition"

Connected through hyperedge HE-2 (induced by source paper "oddball sgd novelty driven stochastic gradient descent for training deep neural networks"):
  with members:
    - "dither is better than dropout for regularising deep neural networks"
    - "taming the relu with parallel dither in a deep neural network"
    - "parallel dither and dropout for regularising deep neural networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-3 (induced by source paper "deep transform time domain audio error correction via probabilistic re synthesis"):
  with members:
    - "deep transform error correction via probabilistic re synthesis"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "deep big simple neural nets for handwritten digit recognition"

Connected through hyperedge HE-4 (induced by source paper "dither is better than dropout for regularising deep neural networks"):
  with members:
    - "over sampling in a deep neural network"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)