human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "variations on the chebyshev lagrange activation function"):
  Abstract: We seek to improve the data efficiency of neural networks and present novel implementations of parameterized piece-wise polynomial activation functions. The parameters are the y-coordinates of n+1 Chebyshev nodes per hidden unit and Lagrangian interpolation between the nodes produces the polynomial on [-1, 1]. We show results for different methods of handling inputs outside [-1, 1] on synthetic datasets, finding significant improvements in capacity of expression and accuracy of interpolation in models that compute some form of linear extrapolation from either ends. We demonstrate competitive or state-of-the-art performance on the classification of images (MNIST and CIFAR-10) and minimally-correlated vectors (DementiaBank) when we replace ReLU or tanh with linearly extrapolated Chebyshev-Lagrange activations in deep residual architectures.

Includes members:
  Member "detecting linguistic characteristics of alzheimer s dementia by interpreting neural models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "enriching neural models with targeted features for dementia detection")
  Member "spectral multigraph networks for discovering and fusing relationships in molecules":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scattering gcn overcoming oversmoothness in graph convolutional networks")
  Member "polynomial regression as an alternative to neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "no spurious local minima in deep quadratic networks")
  Member "semi supervised classification by reaching consensus among modalities":
    also connected through other hyperedges:
      (none)
  Member "the effect of heterogeneous data for alzheimer s disease detection from speech":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lexical features are more vulnerable syntactic features have more predictive power")
  Member "deepconsensus using the consensus of features from multiple layers to attain robust image classification":
    also connected through other hyperedges:
      (none)
  Member "on the importance of normative data in speech based assessment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lexical features are more vulnerable syntactic features have more predictive power")
  Member "shake shake regularization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimized generic feature learning for few shot classification across domains")

Question: Please tell me which class the source paper 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)