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: "adaptive blending units trainable activation functions for deep neural networks"
  Abstract: The most widely used activation functions in current deep feed-forward neural networks are rectified linear units (ReLU), and many alternatives have been successfully applied, as well. However, none of the alternatives have managed to consistently outperform the rest and there is no unified theory connecting properties of the task and network with properties of activation functions for most efficient training. A possible solution is to have the network learn its preferred activation functions. In this work, we introduce Adaptive Blending Units (ABUs), a trainable linear combination of a set of activation functions. Since ABUs learn the shape, as well as the overall scaling of the activation function, we also analyze the effects of adaptive scaling in common activation functions. We experimentally demonstrate advantages of both adaptive scaling and ABUs over common activation functions across a set of systematically varied network specifications. We further show that adaptive scaling works by mitigating covariate shifts during training, and that the observed advantages in performance of ABUs likewise rely largely on the activation function's ability to adapt over the course of training.

Connected through hyperedge HE-1 (induced by source paper "deeplabnet end to end learning of deep radial basis networks with fully learnable basis functions"):
  with members:
    - "deep rbf networks revisited robust classification with rejection"
    - "deep rbfnet point cloud feature learning using radial basis functions"
    - "ferminets learning generative machines to generate efficient neural networks via generative synthesis"
    - "squeeze and excitation networks"
    - "searching for activation functions"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a simple and efficient architecture for trainable activation functions"):
  with members:
    - "self normalizing neural networks"
    - "neural network with unbounded activation functions is universal approximator"
    - "searching for activation functions"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a single hidden layer feedforward network with only one neuron in the hidden layer can approximate any univariate function"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "the power of depth for feedforward neural networks"
    - "deep learning with s shaped rectified linear activation units"

Connected through hyperedge HE-3 (induced by source paper "learning neural activations"):
  with members:
    - "a simple and efficient architecture for trainable activation functions"
    - "mish a self regularized non monotonic neural activation function"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "swish a self gated activation function"
    - "learning to learn by gradient descent by gradient descent"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "polyneuron automatic neuron discovery via learned polyharmonic spline activations"):
  with members:
    - "ferminets learning generative machines to generate efficient neural networks via generative synthesis"
    - "searching for activation functions"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "learning activation functions to improve deep neural networks"

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)