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: "survey of dropout methods for deep neural networks"
  Abstract: Dropout methods are a family of stochastic techniques used in neural network training or inference that have generated significant research interest and are widely used in practice. They have been successfully applied in neural network regularization, model compression, and in measuring the uncertainty of neural network outputs. While original formulated for dense neural network layers, recent advances have made dropout methods also applicable to convolutional and recurrent neural network layers. This paper summarizes the history of dropout methods, their various applications, and current areas of research interest. Important proposed methods are described in additional detail.

Connected through hyperedge HE-1 (induced by source paper "dual attention guided dropblock module for weakly supervised object localization"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "self produced guidance for weakly supervised object localization"
    - "dropblock a regularization method for convolutional networks"
    - "adversarial complementary learning for weakly supervised object localization"
    - "cbam convolutional block attention module"
    - "dual attention network for scene segmentation"
    - "tell me where to look guided attention inference network"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-2 (induced by source paper "distributed learning of deep neural networks using independent subnet training"):
  with members:
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "mlsys the new frontier of machine learning systems"
    - "large batch training for lstm and beyond"
    - "communication efficient distributed sgd with sketching"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "inefficiency of k fac for large batch size training"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "understanding the disharmony between dropout and batch normalization by variance shift"

Connected through hyperedge HE-3 (induced by source paper "rotationout as a regularization method for neural network"):
  with members:
    - "group normalization"
    - "dropblock a regularization method for convolutional networks"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "towards understanding regularization in batch normalization"
    - "network dissection quantifying interpretability of deep visual representations"
    - "focal loss for dense object detection"
    - "dynamic routing between capsules"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-4 (induced by source paper "regularizing neural networks by stochastically training layer ensembles"):
  with members:
    - "improving neural networks by preventing co adaptation of feature detectors"

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)