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 "reliability validation of learning enabled vehicle tracking"):
  Abstract: This paper studies the reliability of a real-world learning-enabled system, which conducts dynamic vehicle tracking based on a high-resolution wide-area motion imagery input. The system consists of multiple neural network components -- to process the imagery inputs -- and multiple symbolic (Kalman filter) components -- to analyse the processed information for vehicle tracking. It is known that neural networks suffer from adversarial examples, which make them lack robustness. However, it is unclear if and how the adversarial examples over learning components can affect the overall system-level reliability. By integrating a coverage-guided neural network testing tool, DeepConcolic, with the vehicle tracking system, we found that (1) the overall system can be resilient to some adversarial examples thanks to the existence of other components, and (2) the overall system presents an extra level of uncertainty which cannot be determined by analysing the deep learning components only. This research suggests the need for novel verification and validation methods for learning-enabled systems.

Includes members:
  Member "test metrics for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "testrnn coverage guided testing on recurrent neural networks")
  Member "reasoning about cognitive trust in stochastic multiagent systems":
    also connected through other hyperedges:
      (none)
  Member "adversarial sample detection for deep neural network through model mutation testing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non intrusive detection of adversarial deep learning attacks via observer networks")
  Member "detecting and tracking small moving objects in wide area motion imagery wami using convolutional neural networks cnns":
    also connected through other hyperedges:
      (none)
  Member "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a safety framework for critical systems utilising deep neural networks")
  Member "symbolic execution for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "importance driven deep learning system testing")
  Member "identifying implementation bugs in machine learning based image classifiers using metamorphic testing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object based metamorphic testing through image structuring")
  Member "deepmutation mutation testing of deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "importance driven deep learning system testing")

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.CV(Computer Vision and Pattern Recognition)