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: "symbolic execution for deep neural networks"
  Abstract: Deep Neural Networks (DNN) are increasingly used in a variety of applications, many of them with substantial safety and security concerns. This paper introduces DeepCheck, a new approach for validating DNNs based on core ideas from program analysis, specifically from symbolic execution. The idea is to translate a DNN into an imperative program, thereby enabling program analysis to assist with DNN validation. A basic translation however creates programs that are very complex to analyze. DeepCheck introduces novel techniques for lightweight symbolic analysis of DNNs and applies them in the context of image classification to address two challenging problems in DNN analysis: 1) identification of important pixels (for attribution and adversarial generation); and 2) creation of 1-pixel and 2-pixel attacks. Experimental results using the MNIST data-set show that DeepCheck's lightweight symbolic analysis provides a valuable tool for DNN validation.

Connected through hyperedge HE-1 (induced by source paper "importance driven deep learning system testing"):
  with members:
    - "input prioritization for testing neural networks"
    - "deepfault fault localization for deep neural networks"
    - "machine learning testing survey landscapes and horizons"
    - "formal security analysis of neural networks using symbolic intervals"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"

Connected through hyperedge HE-2 (induced by source paper "parot a practical framework for robust deep neural network training"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "a provable defense for deep residual networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "mixtrain scalable training of formally robust neural networks"
    - "using machine learning safely in automotive software an assessment and adaption of software process requirements in iso 26262"
    - "with friends like these who needs adversaries"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "reliability validation of learning enabled vehicle tracking"):
  with members:
    - "test metrics for recurrent neural networks"
    - "reasoning about cognitive trust in stochastic multiagent systems"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "detecting and tracking small moving objects in wide area motion imagery wami using convolutional neural networks cnns"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "metamorphic testing for object detection systems"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "yolact real time instance segmentation"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deep learning for generic object detection a survey"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-5 (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"
    - "deepgauge multi granularity testing criteria for deep learning systems"

Connected through hyperedge HE-6 (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"
    - "an empirical study of fault localization families and their combinations"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "detecting adversarial samples for deep neural networks through mutation testing"

Connected through hyperedge HE-7 (induced by source paper "test metrics for recurrent neural networks"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "recurrent neural networks for time series forecasting"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "on adversarial examples for character level neural machine translation"
    - "deepcruiser automated guided testing for stateful deep learning systems"
    - "towards dependability metrics for neural networks"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "deepfault fault localization for deep neural networks"):
  with members:
    - "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"
    - "combinatorial testing for deep learning systems"
    - "testing deep neural networks"
    - "a game based approximate verification of deep neural networks with provable guarantees"

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)