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: "finding faster configurations using flash"
  Abstract: Finding good configurations for a software system is often challenging since the number of configuration options can be large. Software engineers often make poor choices about configuration or, even worse, they usually use a sub-optimal configuration in production, which leads to inadequate performance. To assist engineers in finding the (near) optimal configuration, this paper introduces FLASH, a sequential model-based method, which sequentially explores the configuration space by reflecting on the configurations evaluated so far to determine the next best configuration to explore. FLASH scales up to software systems that defeat the prior state of the art model-based methods in this area. FLASH runs much faster than existing methods and can solve both single-objective and multi-objective optimization problems. The central insight of this paper is to use the prior knowledge (gained from prior runs) to choose the next promising configuration. This strategy reduces the effort (i.e., number of measurements) required to find the (near) optimal configuration. We evaluate FLASH using 30 scenarios based on 7 software systems to demonstrate that FLASH saves effort in 100% and 80% of cases in single-objective and multi-objective problems respectively by up to several orders of magnitude compared to the state of the art techniques.

Connected through hyperedge HE-1 (induced by source paper "learning software configuration spaces a systematic literature review"):
  with members:
    - "automated search for configurations of deep neural network architectures"
    - "machine learning meets quantitative planning enabling self adaptation in autonomous robots"
    - "towards adversarial configurations for software product lines"
    - "an efficient method for uncertainty propagation in robust software performance estimation"
    - "transfer learning for improving model predictions in highly configurable software"
    - "using bad learners to find good configurations"
    - "on the relation of external and internal feature interactions a case study"
    - "faster discovery of faster system configurations with spectral learning"

Connected through hyperedge HE-2 (induced by source paper "whence to learn transferring knowledge in configurable systems using beetle"):
  with members:
    - "micky a cheaper alternative for selecting cloud instances"
    - "scout an experienced guide to find the best cloud configuration"
    - "transfer learning for improving model predictions in highly configurable software"
    - "bellwethers a baseline method for transfer learning"
    - "using bad learners to find good configurations"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "faster discovery of faster system configurations with spectral learning"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"

Connected through hyperedge HE-3 (induced by source paper "how to dodge complex software analytics"):
  with members:
    - "applications of psychological science for actionable analytics"
    - "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"
    - "better software analytics via duo data mining algorithms using used by optimizers"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "is one hyperparameter optimizer enough"
    - "predicting good configurations for github and stack overflow topic models"

Connected through hyperedge HE-4 (induced by source paper "automated search for configurations of deep neural network architectures"):
  with members:
    - "fundamentals of recurrent neural network rnn and long short term memory lstm network"
    - "efficient neural architecture search via parameter sharing"
    - "searching for test data with feature diversity"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "a comparison of 10 sampling algorithms for configurable systems"
    - "designing neural network architectures using reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "software engineering for fairness a case study with hyperparameter optimization"):
  with members:
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "mitigating unwanted biases with adversarial learning"
    - "automated directed fairness testing"
    - "hyperparameter optimization for effort estimation"
    - "a convex framework for fair regression"
    - "fairness testing testing software for discrimination"
    - "equality of opportunity in supervised learning"
    - "fairtest discovering unwarranted associations in data driven applications"

Connected through hyperedge HE-6 (induced by source paper "v2 fast detection of configuration drift in python"):
  with members:
    - "dockerizeme automatic inference of environment dependencies for python code snippets"
    - "gistable evaluating the executability of python code snippets on github"
    - "how to design a program repair bot insights from the repairnator project"
    - "tortoise interactive system configuration repair"
    - "stack overflow in github any snippets there"
    - "from query to usable code an analysis of stack overflow code snippets"
    - "a quantitative study of java software buildability"

Connected through hyperedge HE-7 (induced by source paper "predicting performance of software configurations there is no silver bullet"):
  with members:
    - "using bad learners to find good configurations"
    - "easy over hard a case study on deep learning"
    - "faster discovery of faster system configurations with spectral learning"
    - "a comparison of 10 sampling algorithms for configurable systems"
    - "tuning for software analytics"
    - "distribution aware sampling and weighted model counting for sat"

Connected through hyperedge HE-8 (induced by source paper "sequential model optimization for software process control"):
  with members:
    - "data driven search based software engineering"
    - "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"

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)