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: "a continuum among logarithmic linear and exponential functions and its potential to improve generalization in neural networks"
  Abstract: We present the soft exponential activation function for artificial neural networks that continuously interpolates between logarithmic, linear, and exponential functions. This activation function is simple, differentiable, and parameterized so that it can be trained as the rest of the network is trained. We hypothesize that soft exponential has the potential to improve neural network learning, as it can exactly calculate many natural operations that typical neural networks can only approximate, including addition, multiplication, inner product, distance, polynomials, and sinusoids.

Connected through hyperedge HE-1 (induced by source paper "goldilocks neural networks"):
  with members:
    - "improving deep learning by inverse square root linear units isrlus"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "swish a self gated activation function"
    - "deep learning with s shaped rectified linear activation units"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "empirical evaluation of rectified activations in convolutional network"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving public release of datasets for support vector machine classification"):
  with members:
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "gaussian error linear units gelus"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "functional mechanism regression analysis under differential privacy"
    - "learning in a large function space privacy preserving mechanisms for svm learning"

Connected through hyperedge HE-3 (induced by source paper "adaptive blending units trainable activation functions for deep neural networks"):
  with members:
    - "learning combinations of activation functions"
    - "e swish adjusting activations to different network depths"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "self normalizing neural networks"
    - "searching for activation functions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "neuro symbolic execution the feasibility of an inductive approach to symbolic execution"):
  with members:
    - "a counterexample guided approach to finding numerical invariants"
    - "angora efficient fuzzing by principled search"
    - "loopinvgen a loop invariant generator based on precondition inference"
    - "end to end learning for self driving cars"
    - "an overview of gradient descent optimization algorithms"
    - "tensorflow a system for large scale machine learning"
    - "counterexample guided polynomial loop invariant generation by lagrange interpolation"
    - "the limitations of deep learning in adversarial settings"

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)