human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "analyzing recurrent neural network by probabilistic abstraction"):
  Abstract: Neural network is becoming the dominant approach for solving many real-world problems like computer vision and natural language processing due to its exceptional performance as an end-to-end solution. However, deep learning models are complex and work in a black-box manner in general. This hinders humans from understanding how such systems make decisions or analyzing them using traditional software analysis techniques like testing and verification. To solve this problem and bridge the gap, several recent approaches have proposed to extract simple models in the form of finite-state automata or weighted automata for human understanding and reasoning. The results are however not encouraging due to multiple reasons like low accuracy and scalability issue. In this work, we propose to extract models in the form of probabilistic automata from recurrent neural network models instead. Our work distinguishes itself from existing approaches in two important ways. One is that we extract probabilistic models to compensate for the limited expressiveness of simple models (compared to that of deep neural networks). This is inspired by the observation that human reasoning is often `probabilistic'. The other is that we identify the right level of abstraction based on hierarchical clustering so that the models are extracted in a task-specific way. We conducted experiments on several real-world datasets using state-of-the-art RNN architectures including GRU and LSTM. The result shows that our approach improves existing model extraction approaches significantly and can produce simple models which accurately mimic the original models.

Includes members:
  Member "adversarial sample detection for deep neural network through model mutation testing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reliability validation of learning enabled vehicle tracking")
  Member "deepmutation mutation testing of deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "importance driven deep learning system testing")
  Member "concolic testing for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "importance driven deep learning system testing")
  Member "explaining black boxes on sequential data using weighted automata":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weighted automata extraction from recurrent neural networks via regression on state spaces")
  Member "learning with interpretable structure from rnn":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "assessing the memory ability of recurrent neural networks")
  Member "reachability analysis of deep neural networks with provable guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")
  Member "safety and trustworthiness of deep neural networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "importance driven deep learning system testing")
  Member "an empirical evaluation of rule extraction from recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model")

Question: Please tell me which class the source paper 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)