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: "swish a self gated activation function"
  Abstract: The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various alternatives to ReLU have been proposed, none have managed to replace it due to inconsistent gains. In this work, we propose a new activation function, named Swish, which is simply $f(x) = x \cdot \text{sigmoid}(x)$. Our experiments show that Swish tends to work better than ReLU on deeper models across a number of challenging datasets. For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9% for Mobile NASNet-A and 0.6% for Inception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network.

Connected through hyperedge HE-1 (induced by source paper "meta learning curiosity algorithms"):
  with members:
    - "evolving loss functions with multivariate taylor polynomial parameterizations"
    - "few shot bayesian imitation learning with logical program policies"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "self supervised exploration via disagreement"
    - "few shot bayesian imitation learning with logic over programs"
    - "discovery of useful questions as auxiliary tasks"
    - "world discovery models"
    - "weight agnostic neural networks"

Connected through hyperedge HE-2 (induced by source paper "hyper parameter optimization a review of algorithms and applications"):
  with members:
    - "a generalized framework for population based training"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "meta learning a survey"
    - "deep learning using rectified linear units relu"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "massively parallel hyperparameter tuning"
    - "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (induced by source paper "π nets deep polynomial neural networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "polygan high order polynomial generators"
    - "on the convergence of adam and beyond"
    - "semantic image synthesis with spatially adaptive normalization"
    - "on the expressive power of deep polynomial neural networks"
    - "adversarial training of partially invertible variational autoencoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "residual networks of residual networks multilevel residual networks"

Connected through hyperedge HE-4 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks"):
  with members:
    - "proxy tasks and subjective measures can be misleading in evaluating explainable ai systems"
    - "segment integrated gradients better attributions through regions"
    - "on identifiability in transformers"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "learning pairwise interactions with bayesian neural networks"
    - "incorporating priors with feature attribution on text classification"
    - "learning explainable models using attribution priors"
    - "is attention interpretable"

Connected through hyperedge HE-5 (induced by source paper "soft root sign activation function"):
  with members:
    - "mish a self regularized non monotonic neural activation function"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "towards gan benchmarks which require generalization"):
  with members:
    - "quantitatively evaluating gans with divergences proposed for training"
    - "a call for clarity in reporting bleu scores"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "deep reinforcement learning that matters"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "comparison of maximum likelihood and gan based training of real nvps"

Connected through hyperedge HE-7 (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"
    - "mish a self regularized non monotonic neural activation function"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-8 (induced by source paper "fdftnet facing off fake images using fake detection fine tuning network"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "sliced wasserstein generative models"
    - "few shot adversarial learning of realistic neural talking head models"
    - "searching for mobilenetv3"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a quantization friendly separable convolution for mobilenets"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "meta transfer learning for few shot learning"

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.NE(Neural and Evolutionary Computing)