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: "testing scientific software a systematic literature review"
  Abstract: Context: Scientific software plays an important role in critical decision making, for example making weather predictions based on climate models, and computation of evidence for research publications. Recently, scientists have had to retract publications due to errors caused by software faults. Systematic testing can identify such faults in code. #R##N#Objective: This study aims to identify specific challenges, proposed solutions, and unsolved problems faced when testing scientific software. #R##N#Method: We conducted a systematic literature survey to identify and analyze relevant literature. We identified 62 studies that provided relevant information about testing scientific software. #R##N#Results: We found that challenges faced when testing scientific software fall into two main categories: (1) testing challenges that occur due to characteristics of scientific software such as oracle problems and (2) testing challenges that occur due to cultural differences between scientists and the software engineering community such as viewing the code and the model that it implements as inseparable entities. In addition, we identified methods to potentially overcome these challenges and their limitations. Finally we describe unsolved challenges and how software engineering researchers and practitioners can help to overcome them. #R##N#Conclusions: Scientific software presents special challenges for testing. Specifically, cultural differences between scientist developers and software engineers, along with the characteristics of the scientific software make testing more difficult. Existing techniques such as code clone detection can help to improve the testing process. Software engineers should consider special challenges posed by scientific software such as oracle problems when developing testing techniques.

Connected through hyperedge HE-1 (induced by source paper "experience in engineering of scientific software the case of an optimization software for oil pipelines"):
  with members:
    - "a survey on software testability"

Connected through hyperedge HE-2 (induced by source paper "a systematic mapping study on testing of machine learning programs"):
  with members:
    - "landscaping systematic mapping studies in software engineering a tertiary study"
    - "formal security analysis of neural networks using symbolic intervals"
    - "towards dependability metrics for neural networks"
    - "efficient formal safety analysis of neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "feature guided black box safety testing of deep neural networks"
    - "safety verification of deep neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "challenges for verifying and validating scientific software in computational materials science"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "best practices for scientific computing"

Connected through hyperedge HE-4 (induced by source paper "a systematic literature review of test breakage prevention and repair techniques"):
  with members:
    - "landscaping systematic mapping studies in software engineering a tertiary study"

Connected through hyperedge HE-5 (induced by source paper "refinement type contracts for verification of scientific investigative software"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"

Connected through hyperedge HE-6 (induced by source paper "software engineering challenges of deep learning"):
  with members:
    - "one model to learn them all"
    - "database meets deep learning challenges and opportunities"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "crypto nets neural networks over encrypted data"
    - "caffe convolutional architecture for fast feature embedding"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-7 (induced by source paper "compiler testing a systematic literature analysis"):
  with members:
    - "automated localization for unreproducible builds"
    - "what causes my test alarm automatic cause analysis for test alarms in system and integration testing"
    - "skeletal program enumeration for rigorous compiler testing"
    - "textrank based search term identification for software change tasks"
    - "a new approach to analyzing patterns of collaboration in co authorship networks mesoscopic analysis and interpretation"

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)