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 neural network architecture combining gated recurrent unit gru and support vector machine svm for intrusion detection in network traffic data"
  Abstract: Gated Recurrent Unit (GRU) is a recently-developed variation of the long short-term memory (LSTM) unit, both of which are variants of recurrent neural network (RNN). Through empirical evidence, both models have been proven to be effective in a wide variety of machine learning tasks such as natural language processing, speech recognition, and text classification. Conventionally, like most neural networks, both of the aforementioned RNN variants employ the Softmax function as its final output layer for its prediction, and the cross-entropy function for computing its loss. In this paper, we present an amendment to this norm by introducing linear support vector machine (SVM) as the replacement for Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. While there have been similar studies, this proposal is primarily intended for binary classification on intrusion detection using the 2013 network traffic data from the honeypot systems of Kyoto University. Results show that the GRU-SVM model performs relatively higher than the conventional GRU-Softmax model. The proposed model reached a training accuracy of a81.54% and a testing accuracy of a84.15%, while the latter was able to reach a training accuracy of a63.07% and a testing accuracy of a70.75%. In addition, the juxtaposition of these two final output layers indicate that the SVM would outperform Softmax in prediction time - a theoretical implication which was supported by the actual training and testing time in the study.

Connected through hyperedge HE-1 (induced by source paper "a content based deep intrusion detection system"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "a survey of techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "embedding visual hierarchy with deep networks for large scale visual recognition"

Connected through hyperedge HE-3 (induced by source paper "deep learning using rectified linear units relu"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "attention based models for speech recognition"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "adam a method for stochastic optimization"
    - "deep learning using linear support vector machines"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-4 (induced by source paper "towards building an intelligent anti malware system a deep learning approach using support vector machine svm for malware classification"):
  with members:
    - "random forest for malware classification"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "deep learning using linear support vector machines"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-5 (induced by source paper "on breast cancer detection an application of machine learning algorithms on the wisconsin diagnostic dataset"):
  with members:
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "deep learning using linear support vector machines"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-6 (induced by source paper "an architecture combining convolutional neural network cnn and support vector machine svm for image classification"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "attention based models for speech recognition"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "adam a method for stochastic optimization"
    - "deep learning using linear support vector machines"

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)