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: "transfer learning for improving model predictions in highly configurable software"
  Abstract: Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost. We define a cost model that transform the traditional view of model learning into a multi-objective problem that not only takes into account model accuracy but also measurements effort as well. We evaluate our cost-aware transfer learning solution using real-world configurable software including (i) a robotic system, (ii) 3 different stream processing applications, and (iii) a NoSQL database system. The experimental results demonstrate that our approach can achieve (a) a high prediction accuracy, as well as (b) a high model reliability.

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"
    - "finding faster configurations using flash"
    - "towards adversarial configurations for software product lines"
    - "an efficient method for uncertainty propagation in robust software performance estimation"
    - "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:
    - "finding faster configurations using flash"
    - "micky a cheaper alternative for selecting cloud instances"
    - "scout an experienced guide to find the best cloud configuration"
    - "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 "on using retrained and incremental machine learning for modeling performance of adaptable software an empirical comparison"):
  with members:
    - "a survey and taxonomy of self aware and self adaptive cloud autoscaling systems"
    - "femosaa feature guided and knee driven multi objective optimization for self adaptive software"
    - "self adaptive trade off decision making for autoscaling cloud based services"
    - "daleel simplifying cloud instance selection using machine learning"

Connected through hyperedge HE-4 (induced by source paper "machine learning meets quantitative planning enabling self adaptation in autonomous robots"):
  with members:
    - "a learning approach to enhance assurances for real time self adaptive systems"
    - "faster discovery of faster system configurations with spectral learning"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"

Connected through hyperedge HE-5 (induced by source paper "feature model guided online learning for self adaptive systems"):
  with members:
    - "a comparison of reinforcement learning techniques for fuzzy cloud auto scaling"
    - "reinforcement learning a survey"

Connected through hyperedge HE-6 (induced by source paper "data driven search based software engineering"):
  with members:
    - "finding faster configurations using flash"
    - "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"
    - "finding better active learners for faster literature reviews"
    - "short er reasoning about larger requirements models"
    - "using bad learners to find good configurations"
    - "better data is better than better data miners benefits of tuning smote for defect prediction"

Connected through hyperedge HE-7 (induced by source paper "a learning approach to enhance assurances for real time self adaptive systems"):
  with members:
    - "perpetual assurances for self adaptive systems"
    - "engineering trustworthy self adaptive software with dynamic assurance cases"
    - "statistical model checking an overview"

Connected through hyperedge HE-8 (induced by source paper "an efficient method for uncertainty propagation in robust software performance estimation"):
  with members:
    - "an uncertainty aware approach to optimal configuration of stream processing systems"
    - "performance oriented devops a research agenda"

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)