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: "metamorphic testing a new approach for generating next test cases"
  Abstract: In software testing, a set of test cases is constructed according to some predefined selection criteria. The software is then examined against these test cases. Three interesting observations have been made on the current artifacts of software testing. Firstly, an error-revealing test case is considered useful while a successful test case which does not reveal software errors is usually not further investigated. Whether these successful test cases still contain useful information for revealing software errors has not been properly studied. Secondly, no matter how extensive the testing has been conducted in the development phase, errors may still exist in the software [5]. These errors, if left undetected, may eventually cause damage to the production system. The study of techniques for uncovering software errors in the production phase is seldom addressed in the literature. Thirdly, as indicated by Weyuker in [6], the availability of test oracles is pragmatically unattainable in most situations. However, the availability of test oracles is generally assumed in conventional software testing techniques. In this paper, we propose a novel test case selection technique that derives new test cases from the successful ones. The selection aims at revealing software errors that are possibly left undetected in successful test cases which may be generated using some existing strategies. As such, the proposed technique augments the effectiveness of existing test selection strategies. The technique also helps uncover software errors in the production phase and can be used in the absence of test oracles.

Connected through hyperedge HE-1 (induced by source paper "object based metamorphic testing through image structuring"):
  with members:
    - "adaptive metamorphic testing with contextual bandits"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"

Connected through hyperedge HE-2 (induced by source paper "testing database engines via pivoted query synthesis"):
  with members:
    - "skeletal program enumeration for rigorous compiler testing"

Connected through hyperedge HE-3 (induced by source paper "metamorphic testing for object detection systems"):
  with members:
    - "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"
    - "symbolic execution for deep neural networks"
    - "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-4 (induced by source paper "structure invariant testing for machine translation"):
  with members:
    - "machine learning testing survey landscapes and horizons"
    - "defensive quantization when efficiency meets robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "code2vec learning distributed representations of code"
    - "on adversarial examples for character level neural machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"

Connected through hyperedge HE-5 (induced by source paper "testing deep learning models for image analysis using object relevant metamorphic relations"):
  with members:
    - "bag of freebies for training object detection neural networks"
    - "cornernet lite efficient keypoint based object detection"
    - "bag of tricks for image classification with convolutional neural networks"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "yolov3 an incremental improvement"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "simple baselines for human pose estimation and tracking"
    - "deepmutation mutation testing of deep learning systems"

Connected through hyperedge HE-6 (induced by source paper "adaptive metamorphic testing with contextual bandits"):
  with members:
    - "metamorphic detection of adversarial examples in deep learning models with affine transformations"
    - "metamorphic testing for quality assurance of protein function prediction tools"
    - "fault detection effectiveness of metamorphic relations developed for testing supervised classifiers"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-7 (induced by source paper "towards structured evaluation of deep neural network supervisors"):
  with members:
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"
    - "guiding deep learning system testing using surprise adequacy"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "deeproad gan based metamorphic autonomous driving system testing"
    - "on calibration of modern neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "an analysis of iso 26262 using machine learning safely in automotive software"

Connected through hyperedge HE-8 (induced by source paper "automatic testing and improvement of machine translation"):
  with members:
    - "machine learning testing survey landscapes and horizons"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "modeling recurrence for transformer"
    - "robust neural machine translation with doubly adversarial inputs"
    - "on the impact of various types of noise on neural machine translation"
    - "towards robust neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "guiding neural machine translation with retrieved translation pieces"

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)