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: "mish a self regularized non monotonic neural activation function"
  Abstract: The concept of non-linearity in a Neural Network is introduced by an activation function which serves an integral role in the training and performance evaluation of the network. Over the years of theoretical research, many activation functions have been proposed, however, only a few are widely used in mostly all applications which include ReLU (Rectified Linear Unit), TanH (Tan Hyperbolic), Sigmoid, Leaky ReLU and Swish. In this work, a novel neural activation function called as Mish is proposed. The experiments show that Mish tends to work better than both ReLU and Swish along with other standard activation functions in many deep networks across challenging datasets. For instance, in Squeeze Excite Net- 18 for CIFAR 100 classification, the network with Mish had an increase in Top-1 test accuracy by 0.494% and 1.671% as compared to the same network with Swish and ReLU respectively. The similarity to Swish along with providing a boost in performance and its simplicity in implementation makes it easier for researchers and developers to use Mish in their Neural Network Models.

Connected through hyperedge HE-1 (induced by source paper "soft root sign activation function"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "improving deep learning by inverse square root linear units isrlus"
    - "invertible residual networks"
    - "the quest for the golden activation function"
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "attention is all you need"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "fastai a layered api for deep learning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "developing an app to interpret chest x rays to support the diagnosis of respiratory pathology with artificial intelligence"
    - "suggestion mining from online reviews using ulmfit"
    - "multiclass language identification using deep learning on spectral images of audio signals"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "bag of tricks for image classification with convolutional neural networks"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"

Connected through hyperedge HE-3 (induced by source paper "attention a lightweight 2d hand pose estimation approach"):
  with members:
    - "real time facial surface geometry from monocular video on mobile gpus"
    - "making convolutional networks shift invariant again"
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "3d hand shape and pose from images in the wild"
    - "attention augmented convolutional networks"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-4 (induced by source paper "learning neural activations"):
  with members:
    - "a simple and efficient architecture for trainable activation functions"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "adaptive blending units trainable activation functions for deep neural networks"
    - "swish a self gated activation function"
    - "learning to learn by gradient descent by gradient descent"
    - "adam a method for stochastic optimization"

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)