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: "test metrics for recurrent neural networks"
  Abstract: Recurrent neural networks (RNNs) have been applied to a broad range of application areas such as natural language processing, drug discovery, and video recognition. This paper develops a coverage-guided test framework, including three test metrics and a mutation-based test case generation method, for the validation of a major class of RNNs, i.e., long short-term memory networks (LSTMs). The test metrics are designed with respect to the internal structures of the LSTM layers to quantify the information of the forget gate, the one-step information change of an aggregate hidden state, and the multi-step information evolution of positive and negative aggregate hidden state, respectively. We apply the test framework to several typical LSTM applications, including a network trained on IMDB movie reviews for sentiment analysis, a network trained on MNIST dataset for image classification, and a network trained on a lipophilicity dataset for scientific machine learning. Our experimental results show that the coverage-guided testing can be used to not only extensively exploit the behaviour of the LSTM layer in order to discover the safety loopholes (such as adversarial examples) but also help understand the internal mechanism of how the LSTM layer processes data.

Connected through hyperedge HE-1 (induced by source paper "reliability validation of learning enabled vehicle tracking"):
  with members:
    - "reasoning about cognitive trust in stochastic multiagent systems"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "detecting and tracking small moving objects in wide area motion imagery wami using convolutional neural networks cnns"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "testrnn coverage guided testing on recurrent neural networks"):
  with members:
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deepcruiser automated guided testing for stateful deep learning systems"
    - "concolic testing for deep neural networks"
    - "testing deep neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "feature guided black box safety testing of deep neural networks"
    - "memory visualization for gated recurrent neural networks in speech recognition"
    - "intriguing properties of neural networks"

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)