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: "test set diameter quantifying the diversity of sets of test cases"
  Abstract: A common and natural intuition among software testers is that test cases need to differ if a software system is to be tested properly and its quality ensured. Consequently, much research has gone into formulating distance measures for how test cases, their inputs and/or their outputs differ. However, common to these proposals is that they are data type specific and/or calculate the diversity only between pairs of test inputs, traces or outputs. #R##N#We propose a new metric to measure the diversity of sets of tests: the test set diameter (TSDm). It extends our earlier, pairwise test diversity metrics based on recent advances in information theory regarding the calculation of the normalized compression distance (NCD) for multisets. An advantage is that TSDm can be applied regardless of data type and on any test-related information, not only the test inputs. A downside is the increased computational time compared to competing approaches. #R##N#Our experiments on four different systems show that the test set diameter can help select test sets with higher structural and fault coverage than random selection even when only applied to test inputs. This can enable early test design and selection, prior to even having a software system to test, and complement other types of test automation and analysis. We argue that this quantification of test set diversity creates a number of opportunities to better understand software quality and provides practical ways to increase it.

Connected through hyperedge HE-1 (induced by source paper "boundary value exploration for software analysis"):
  with members:
    - "guiding deep learning system testing using surprise adequacy"
    - "finding a boundary between valid and invalid regions of the input space"
    - "visualizing test diversity to support test optimisation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "faster creation of smaller test suites with snap"):
  with members:
    - "data driven search based software engineering"
    - "hyperparameter optimization for effort estimation"
    - "riot a stochastic based method for workflow scheduling in the cloud"
    - "revisiting unsupervised learning for defect prediction"
    - "beyond evolutionary algorithms for search based software engineering"
    - "a survey of symbolic execution techniques"
    - "sampling as a baseline optimizer for search based software engineering"
    - "enhancing reuse of constraint solutions to improve symbolic execution"

Connected through hyperedge HE-4 (induced by source paper "towards automated boundary value testing with program derivatives and search"):
  with members:
    - "finding a boundary between valid and invalid regions of the input space"
    - "normalized information distance"

Connected through hyperedge HE-5 (induced by source paper "amortising the cost of mutation based fault localisation using statistical inference"):
  with members:
    - "test case purification for improving fault localization"

Connected through hyperedge HE-6 (induced by source paper "guiding deep learning system testing using surprise adequacy"):
  with members:
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "deepmutation mutation testing of deep learning systems"
    - "combinatorial testing for deep learning systems"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"

Connected through hyperedge HE-7 (induced by source paper "test prioritization in continuous integration environments"):
  with members:
    - "reinforcement learning for automatic test case prioritization and selection in continuous integration"
    - "normalized compression distance of multisets with applications"
    - "the similarity metric"

Connected through hyperedge HE-8 (induced by source paper "test case prioritization using test similarities"):
  with members:
    - "test prioritization in continuous integration environments"

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)