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: "is it time to swish comparing deep learning activation functions across nlp tasks"
  Abstract: Activation functions play a crucial role in neural networks because they are the nonlinearities which have been attributed to the success story of deep learning. One of the currently most popular activation functions is ReLU, but several competitors have recently been proposed or 'discovered', including LReLU functions and swish. While most works compare newly proposed activation functions on few tasks (usually from image classification) and against few competitors (usually ReLU), we perform the first large-scale comparison of 21 activation functions across eight different NLP tasks. We find that a largely unknown activation function performs most stably across all tasks, the so-called penalized tanh function. We also show that it can successfully replace the sigmoid and tanh gates in LSTM cells, leading to a 2 percentage point (pp) improvement over the standard choices on a challenging NLP task.

Connected through hyperedge HE-1 (induced by source paper "effect of activation functions on the training of overparametrized neural nets"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the expressive power of deep polynomial neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "activation functions comparison of trends in practice and research for deep learning"

Connected through hyperedge HE-2 (induced by source paper "text processing like humans do visually attacking and shielding nlp systems"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"
    - "shielding google s language toxicity model against adversarial attacks"
    - "cross lingual argumentation mining machine translation and a bit of projection is all you need"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"

Connected through hyperedge HE-3 (induced by source paper "deep learning for csi feedback based on superimposed coding"):
  with members:
    - "learning to detect"
    - "artificial intelligence aided receiver for a cp free ofdm system design simulation and experimental test"
    - "enabling fdd massive mimo through deep learning based channel prediction"
    - "deep learning in physical layer communications"
    - "mimo channel information feedback using deep recurrent network"
    - "multi task learning as multi objective optimization"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "deep learning based csi feedback approach for time varying massive mimo channels"

Connected through hyperedge HE-4 (induced by source paper "activation functions for generalized learning vector quantization a performance comparison"):
  with members:
    - "flatten t swish a thresholded relu swish like activation function for deep learning"
    - "efficient neural network robustness certification with general activation functions"
    - "searching for activation functions"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "swish a self gated activation function"
    - "a data driven functional projection approach for the selection of feature ranges in spectra with ica or cluster analysis"

Connected through hyperedge HE-5 (induced by source paper "overparameterized neural networks can implement associative memory"):
  with members:
    - "deep image prior"
    - "searching for activation functions"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "empirical evaluation of rectified activations in convolutional network"

Connected through hyperedge HE-6 (induced by source paper "hidden unit specialization in layered neural networks relu vs sigmoidal activation"):
  with members:
    - "on line learning dynamics of relu neural networks using statistical physics techniques"
    - "searching for activation functions"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "exact solutions to the nonlinear dynamics of learning in deep linear 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.CL(Computation and Language)