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: "the handbook of engineering self aware and self expressive systems"
  Abstract: When faced with the task of designing and implementing a new self-aware and self-expressive computing system, researchers and practitioners need a set of guidelines on how to use the concepts and foundations developed in the Engineering Proprioception in Computing Systems (EPiCS) project. This report provides such guidelines on how to design self-aware and self-expressive computing systems in a principled way. We have documented different categories of self-awareness and self-expression level using architectural patterns. We have also documented common architectural primitives, their possible candidate techniques and attributes for architecting self-aware and self-expressive systems. Drawing on the knowledge obtained from the previous investigations, we proposed a pattern driven methodology for engineering self-aware and self-expressive systems to assist in utilising the patterns and primitives during design. The methodology contains detailed guidance to make decisions with respect to the possible design alternatives, providing a systematic way to build self-aware and self-expressive systems. Then, we qualitatively and quantitatively evaluated the methodology using two case studies. The results reveal that our pattern driven methodology covers the main aspects of engineering self-aware and self-expressive systems, and that the resulted systems perform significantly better than the non-self-aware systems.

Connected through hyperedge HE-1 (induced by source paper "modelling and simulation environment for self adaptive and self aware cloud architectures"):
  with members:
    - "modeling and simulation of scalable cloud computing environments and the cloudsim toolkit challenges and opportunities"

Connected through hyperedge HE-2 (induced by source paper "a survey and taxonomy of self aware and self adaptive cloud autoscaling systems"):
  with members:
    - "femosaa feature guided and knee driven multi objective optimization for self adaptive software"
    - "self adaptive trade off decision making for autoscaling cloud based services"
    - "a reliable and cost efficient auto scaling system for web applications using heterogeneous spot instances"
    - "auto scaling web applications in clouds a taxonomy and survey"
    - "using application data for sla aware auto scaling in cloud environments"
    - "online qos modeling in the cloud a hybrid and adaptive multi learners approach"
    - "aneka a software platform for net based cloud computing"

Connected through hyperedge HE-3 (induced by source paper "survey and taxonomy of self aware and self adaptive autoscaling systems in the cloud"):
  with members:
    - "self adaptive trade off decision making for autoscaling cloud based services"
    - "auto scaling web applications in clouds a taxonomy and survey"
    - "using application data for sla aware auto scaling in cloud environments"
    - "online qos modeling in the cloud a hybrid and adaptive multi learners approach"

Connected through hyperedge HE-4 (induced by source paper "bridging ecology and cloud transposing ecological prespective to enable better cloud autoscaling"):
  with members:
    - "online qos modeling in the cloud a hybrid and adaptive multi learners approach"
    - "empirical evidence of large scale diversity in api usage of object oriented software"
    - "ecosystem oriented distributed evolutionary computing"

Connected through hyperedge HE-5 (induced by source paper "self aware and self adaptive autoscaling for cloud based services"):
  with members:
    - "online qos modeling in the cloud a hybrid and adaptive multi learners approach"

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)