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: "combinatorial testing for deep learning systems"
  Abstract: Deep learning (DL) has achieved remarkable progress over the past decade and been widely applied to many safety-critical applications. However, the robustness of DL systems recently receives great concerns, such as adversarial examples against computer vision systems, which could potentially result in severe consequences. Adopting testing techniques could help to evaluate the robustness of a DL system and therefore detect vulnerabilities at an early stage. The main challenge of testing such systems is that its runtime state space is too large: if we view each neuron as a runtime state for DL, then a DL system often contains massive states, rendering testing each state almost impossible. For traditional software, combinatorial testing (CT) is an effective testing technique to reduce the testing space while obtaining relatively high defect detection abilities. In this paper, we perform an exploratory study of CT on DL systems. We adapt the concept in CT and propose a set of coverage criteria for DL systems, as well as a CT coverage guided test generation technique. Our evaluation demonstrates that CT provides a promising avenue for testing DL systems. We further pose several open questions and interesting directions for combinatorial testing of DL systems.

Connected through hyperedge HE-1 (induced by source paper "raid randomized adversarial input detection for neural networks"):
  with members:
    - "when explainability meets adversarial learning detecting adversarial examples using shap signatures"
    - "are odds really odd bypassing statistical detection of adversarial examples"
    - "deepsmartfuzzer reward guided test generation for deep learning"
    - "deepfault fault localization for deep neural networks"
    - "a new defense against adversarial images turning a weakness into a strength"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "on evaluating adversarial robustness"
    - "deepgauge multi granularity testing criteria for deep learning systems"

Connected through hyperedge HE-2 (induced by source paper "deepsearch simple and effective blackbox fuzzing of deep neural networks"):
  with members:
    - "towards improved testing for deep learning"
    - "algorithms for verifying deep neural networks"
    - "machine learning testing survey landscapes and horizons"
    - "on evaluating adversarial robustness"
    - "the efficacy of shield under different threat models"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "symbolic execution for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "explaining deep neural networks using spectrum based fault localization"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "deepfault fault localization for deep neural networks"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "an empirical study of fault localization families and their combinations"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "deepfault fault localization for deep neural networks"):
  with members:
    - "symbolic execution for deep neural networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "guiding deep learning system testing using surprise adequacy"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "testing deep neural networks"
    - "a game based approximate verification of deep neural networks with provable guarantees"

Connected through hyperedge HE-5 (induced by source paper "rnn test adversarial testing framework for recurrent neural network systems"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "deepcruiser automated guided testing for stateful deep learning systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deep text classification can be fooled"
    - "interpretable adversarial perturbation in input embedding space for text"
    - "audio adversarial examples targeted attacks on speech to text"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "testing deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "boosting operational dnn testing efficiency through conditioning"):
  with members:
    - "assurances in software testing a roadmap"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "on testing machine learning programs"
    - "guiding deep learning system testing using surprise adequacy"
    - "testing deep neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-7 (induced by source paper "towards improved testing for deep learning"):
  with members:
    - "deepmutation mutation testing of deep learning systems"
    - "testing deep neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "simple black box adversarial perturbations for deep networks"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-8 (induced by source paper "on functional test generation for deep neural network ips"):
  with members:
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "testing deep neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "the limitations of deep learning in adversarial settings"

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.SE(Software Engineering)