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 "data sanity check for deep learning systems via learnt assertions"):
  Abstract: Deep learning (DL) techniques have demonstrated satisfactory performance in many tasks, even in safety-critical applications. Reliability is hence a critical consideration to DL-based systems. However, the statistical nature of DL makes it quite vulnerable to invalid inputs, i.e., those cases that are not considered in the training phase of a DL model. This paper proposes to perform data sanity check to identify invalid inputs, so as to enhance the reliability of DL-based systems. To this end, we design and implement a tool to detect behavior deviation of a DL model when processing an input case, and considers it the symptom of invalid input cases. Via a light, automatic instrumentation to the target DL model, this tool extracts the data flow footprints and conducts an assertion-based validation mechanism.

Includes members:
  Member "deepmutation mutation testing of deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "importance driven deep learning system testing")
  Member "concolic testing for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "importance driven deep learning system testing")
  Member "testing deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "importance driven deep learning system testing")
  Member "deepxplore automated whitebox testing of deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "importance driven deep learning system testing")
  Member "deepsafe a data driven approach for checking adversarial robustness in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deeptest automated testing of deep neural network driven autonomous cars":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "importance driven deep learning system testing")
  Member "a baseline for detecting misclassified and out of distribution examples in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "anomalous instance detection in deep learning a survey")

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)