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: "rmdl random multimodel deep learning for classification"
  Abstract: The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. Deep learning models have achieved state-of-the-art results across many domains. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. RDML can accept as input a variety data to include text, video, images, and symbolic. This paper describes RMDL and shows test results for image and text data including MNIST, CIFAR-10, WOS, Reuters, IMDB, and 20newsgroup. These test results show that RDML produces consistently better performance than standard methods over a broad range of data types and classification problems.

Connected through hyperedge HE-1 (induced by source paper "a branching and merging convolutional network with homogeneous filter capsules"):
  with members:
    - "homogeneous vector capsules enable adaptive gradient descent in convolutional neural networks"
    - "averaging weights leads to wider optima and better generalization"
    - "random erasing data augmentation"
    - "dynamic routing between capsules"
    - "lets keep it simple using simple architectures to outperform deeper and more complex architectures"
    - "densely connected convolutional networks"
    - "batch normalized maxout network in network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "ensemble learning in cnn augmented with fully connected subnetworks"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "random erasing data augmentation"
    - "mixup beyond empirical risk minimization"
    - "improved regularization of convolutional neural networks with cutout"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "apac augmented pattern classification with neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "mobinet a mobile binary network for image classification"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "seeing small faces from robust anchor s perspective"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "quantization mimic towards very tiny cnn for object detection"

Connected through hyperedge HE-4 (induced by source paper "text classification algorithms a survey"):
  with members:
    - "analysis of railway accidents narratives using deep learning"
    - "one shot and few shot learning of word embeddings"
    - "deep contextualized word representations"
    - "an improvement of data classification using random multimodel deep learning rmdl"
    - "gakco a fast gapped k mer string kernel using counting"
    - "learning important features through propagating activation differences"
    - "opening the black box of deep neural networks via information"
    - "improving the accuracy of pre trained word embeddings for sentiment analysis"

Connected through hyperedge HE-5 (induced by source paper "adaptive generation of unrestricted adversarial inputs"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "a general framework for adversarial examples with objectives"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "global adversarial attacks for assessing deep learning robustness"
    - "how good is my gan"
    - "towards the first adversarially robust neural network model on mnist"
    - "mixtrain scalable training of formally robust neural networks"

Connected through hyperedge HE-6 (induced by source paper "analysis of railway accidents narratives using deep learning"):
  with members:
    - "an improvement of data classification using random multimodel deep learning rmdl"
    - "hdltex hierarchical deep learning for text classification"
    - "character level convolutional networks for text classification"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "convolutional neural networks for sentence classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"

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.LG(Machine Learning)