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: "learning with interpretable structure from rnn"
  Abstract: In structure learning, the output is generally a structure that is used as supervision information to achieve good performance. Considering the interpretation of deep learning models has raised extended attention these years, it will be beneficial if we can learn an interpretable structure from deep learning models. In this paper, we focus on Recurrent Neural Networks (RNNs) whose inner mechanism is still not clearly understood. We find that Finite State Automaton (FSA) that processes sequential data has more interpretable inner mechanism and can be learned from RNNs as the interpretable structure. We propose two methods to learn FSA from RNN based on two different clustering methods. We first give the graphical illustration of FSA for human beings to follow, which shows the interpretability. From the FSA's point of view, we then analyze how the performance of RNNs are affected by the number of gates, as well as the semantic meaning behind the transition of numerical hidden states. Our results suggest that RNNs with simple gated structure such as Minimal Gated Unit (MGU) is more desirable and the transitions in FSA leading to specific classification result are associated with corresponding words which are understandable by human beings.

Connected through hyperedge HE-1 (induced by source paper "assessing the memory ability of recurrent neural networks"):
  with members:
    - "evaluating the ability of lstms to learn context free grammars"
    - "exploring the syntactic abilities of rnns with multi task learning"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "language modeling with gated convolutional networks"
    - "using the output embedding to improve language models"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "character aware neural language models"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-2 (induced by source paper "analyzing recurrent neural network by probabilistic abstraction"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "explaining black boxes on sequential data using weighted automata"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "safety and trustworthiness of deep neural networks a survey"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-3 (induced by source paper "interpreting basis path set in neural networks"):
  with members:
    - "visual interpretability for deep learning a survey"
    - "explaining alphago interpreting contextual effects in neural networks"
    - "interpreting cnn knowledge via an explanatory graph"
    - "path sgd path normalized optimization in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "deepcruiser automated guided testing for stateful deep learning systems"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "audio adversarial examples targeted attacks on speech to text"
    - "combinatorial testing for deep learning systems"
    - "secure deep learning engineering a software quality assurance perspective"
    - "testing deep 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.NE(Neural and Evolutionary Computing)