human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deeplabnet end to end learning of deep radial basis networks with fully learnable basis functions"):
  Abstract: From fully connected neural networks to convolutional neural networks, the learned parameters within a neural network have been primarily relegated to the linear parameters (e.g., convolutional filters). The non-linear functions (e.g., activation functions) have largely remained, with few exceptions in recent years, parameter-less, static throughout training, and seen limited variation in design. Largely ignored by the deep learning community, radial basis function (RBF) networks provide an interesting mechanism for learning more complex non-linear activation functions in addition to the linear parameters in a network. However, the interest in RBF networks has waned over time due to the difficulty of integrating RBFs into more complex deep neural network architectures in a tractable and stable manner. In this work, we present a novel approach that enables end-to-end learning of deep RBF networks with fully learnable activation basis functions in an automatic and tractable manner. We demonstrate that our approach for enabling the use of learnable activation basis functions in deep neural networks, which we will refer to as DeepLABNet, is an effective tool for automated activation function learning within complex network architectures.

Includes members:
  Member "adaptive blending units trainable activation functions for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a simple and efficient architecture for trainable activation functions")
  Member "deep rbf networks revisited robust classification with rejection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "classification of traffic using neural networks by rejecting a novel approach in classifying vpn traffic")
  Member "deep rbfnet point cloud feature learning using radial basis functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep mesh reconstruction from single rgb images via topology modification networks")
  Member "ferminets learning generative machines to generate efficient neural networks via generative synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attonets compact and efficient deep neural networks for the edge via human machine collaborative design")
  Member "squeeze and excitation networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "searching for activation functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "refinenet multi path refinement networks for high resolution semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "deep learning with darwin evolutionary synthesis of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "seeing convolution through the eyes of finite transformation semigroup theory an abstract algebraic interpretation of convolutional neural networks")

Question: Please tell me which class the source paper 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)