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: "data driven search based software engineering"
  Abstract: This paper introduces Data-Driven Search-based Software Engineering (DSE), which combines insights from Mining Software Repositories (MSR) and Search-based Software Engineering (SBSE). While MSR formulates software engineering problems as data mining problems, SBSE reformulates SE problems as optimization problems and use meta-heuristic algorithms to solve them. Both MSR and SBSE share the common goal of providing insights to improve software engineering. The algorithms used in these two areas also have intrinsic relationships. We, therefore, argue that combining these two fields is useful for situations (a) which require learning from a large data source or (b) when optimizers need to know the lay of the land to find better solutions, faster. #R##N#This paper aims to answer the following three questions: (1) What are the various topics addressed by DSE? (2) What types of data are used by the researchers in this area? (3) What research approaches do researchers use? The paper briefly sets out to act as a practical guide to develop new DSE techniques and also to serve as a teaching resource. #R##N#This paper also presents a resource (tiny.cc/data-se) for exploring DSE. The resource contains 89 artifacts which are related to DSE, divided into 13 groups such as requirements engineering, software product lines, software processes. All the materials in this repository have been used in recent software engineering papers; i.e., for all this material, there exist baseline results against which researchers can comparatively assess their new ideas.

Connected through hyperedge HE-1 (induced by source paper "faster creation of smaller test suites with snap"):
  with members:
    - "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"
    - "constrained sampling and counting universal hashing meets sat solving"

Connected through hyperedge HE-2 (induced by source paper "sequential model optimization for software process control"):
  with members:
    - "finding faster configurations using flash"
    - "warmstarting of model based algorithm configuration"
    - "what is the connection between issues bugs and enhancements lessons learned from 800 software projects"
    - "negative results for software effort estimation"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "tuning for software analytics"

Connected through hyperedge HE-3 (induced by source paper "better software analytics via duo data mining algorithms using used by optimizers"):
  with members:
    - "finding faster configurations using flash"
    - "applications of psychological science for actionable analytics"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "is one hyperparameter optimizer enough"
    - "an improved generic bet and run strategy for speeding up stochastic local search"
    - "predicting good configurations for github and stack overflow topic models"

Connected through hyperedge HE-4 (induced by source paper "bad smells in software analytics papers"):
  with members:
    - "the impact of automated parameter optimization on defect prediction models"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "we don t need another hero the impact of heroes on software development"
    - "revisiting unsupervised learning for defect prediction"
    - "what is the connection between issues bugs and enhancements lessons learned from 800 software projects"
    - "easy over hard a case study on deep learning"
    - "sampling as a baseline optimizer for search based software engineering"

Connected through hyperedge HE-5 (induced by source paper "predicting good configurations for github and stack overflow topic models"):
  with members:
    - "categorizing the content of github readme files"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "better software analytics via duo data mining algorithms using used by optimizers"
    - "automated algorithm selection survey and perspectives"
    - "sotorrent reconstructing and analyzing the evolution of stack overflow posts"
    - "a case study of algorithm selection for the traveling thief problem"
    - "aslib a benchmark library for algorithm selection"
    - "algorithm selection for combinatorial search problems a survey"

Connected through hyperedge HE-6 (induced by source paper "hyperparameter optimization for effort estimation"):
  with members:
    - "the impact of automated parameter optimization on defect prediction models"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "negative results for software effort estimation"
    - "tuning for software analytics"
    - "locally weighted naive bayes"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-7 (induced by source paper "why software effort estimation needs sbse"):
  with members:
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "a deep learning model for estimating story points"
    - "negative results for software effort estimation"
    - "tuning for software analytics"
    - "locally weighted naive bayes"

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)