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: "femosaa feature guided and knee driven multi objective optimization for self adaptive software"
  Abstract: Self-Adaptive Software (SAS) can reconfigure itself to adapt to the changing environment at runtime, aiming to continually optimize conflicted nonfunctional objectives (e.g., response time, energy consumption, throughput, cost, etc.). In this article, we present Feature-guided and knEe-driven Multi-Objective optimization for Self-Adaptive softwAre (FEMOSAA), a novel framework that automatically synergizes the feature model and Multi-Objective Evolutionary Algorithm (MOEA) to optimize SAS at runtime. FEMOSAA operates in two phases: at design time, FEMOSAA automatically transposes the engineers’ design of SAS, expressed as a feature model, to fit the MOEA, creating new chromosome representation and reproduction operators. At runtime, FEMOSAA utilizes the feature model as domain knowledge to guide the search and further extend the MOEA, providing a larger chance for finding better solutions. In addition, we have designed a new method to search for the knee solutions, which can achieve a balanced tradeoff. We comprehensively evaluated FEMOSAA on two running SAS: One is a highly complex SAS with various adaptable real-world software under the realistic workload trace; another is a service-oriented SAS that can be dynamically composed from services. In particular, we compared the effectiveness and overhead of FEMOSAA against four of its variants and three other search-based frameworks for SAS under various scenarios, including three commonly applied MOEAs, two workload patterns, and diverse conflicting quality objectives. The results reveal the effectiveness of FEMOSAA and its superiority over the others with high statistical significance and nontrivial effect sizes.

Connected through hyperedge HE-1 (induced by source paper "understanding the automated parameter optimization on transfer learning for cpdp an empirical study"):
  with members:
    - "which surrogate works for empirical performance modelling a case study with differential evolution"
    - "visualisation of pareto front approximation a short survey and empirical comparisons"
    - "how to dodge complex software analytics"
    - "transfer learning oriented class imbalance learning for cross project defect prediction"
    - "the impact of automated parameter optimization on defect prediction models"
    - "a systematic mapping study on cross project defect prediction"
    - "evolutionary many objective optimization based on adversarial decomposition"
    - "an empirical study on software defect prediction with a simplified metric set"

Connected through hyperedge HE-2 (induced by source paper "how to evaluate solutions in pareto based search based software engineering a critical review and methodological guidance"):
  with members:
    - "search based software engineering for self adaptive systems one survey five disappointments and six opportunities"
    - "understanding the automated parameter optimization on transfer learning for cpdp an empirical study"
    - "self adaptive trade off decision making for autoscaling cloud based services"
    - "how to read many objective solution sets in parallel coordinates"
    - "sampling as a baseline optimizer for search based software engineering"
    - "elegant object oriented software design via interactive evolutionary computation"

Connected through hyperedge HE-3 (induced by source paper "search based software engineering for self adaptive systems one survey five disappointments and six opportunities"):
  with members:
    - "planning as optimization dynamically discovering optimal configurations for runtime situations"
    - "self adaptive trade off decision making for autoscaling cloud based services"
    - "efficient utility driven self healing employing adaptation rules for large dynamic architectures"
    - "privacy policy negotiation in social media"

Connected through hyperedge HE-4 (induced by source paper "routing led placement of vnfs in arbitrary networks"):
  with members:
    - "evolutionary many objective optimization based on adversarial decomposition"
    - "optimal orchestration of virtual network functions"
    - "stringer balancing latency and resource usage in service function chain provisioning"

Connected through hyperedge HE-5 (induced by source paper "surrogate assisted evolutionary algorithm for medium scale expensive multi objective optimisation problems"):
  with members:
    - "which surrogate works for empirical performance modelling a case study with differential evolution"
    - "visualisation of pareto front approximation a short survey and empirical comparisons"
    - "evolutionary many objective optimization based on adversarial decomposition"

Connected through hyperedge HE-6 (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"
    - "self adaptive trade off decision making for autoscaling cloud based services"
    - "transfer learning for improving model predictions in highly configurable software"
    - "daleel simplifying cloud instance selection using machine learning"

Connected through hyperedge HE-7 (induced by source paper "dynamic adaptive network configuration for iot systems a search based approach"):
  with members:
    - "online multicast traffic engineering for software defined networks"
    - "model based testing iot communication via active automata learning"

Connected through hyperedge HE-8 (induced by source paper "does preference always help a holistic study on preference based evolutionary multi objective optimisation using reference points"):
  with members:
    - "evolutionary many objective optimization based on adversarial decomposition"

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)