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: "self adaptive trade off decision making for autoscaling cloud based services"
  Abstract: Elasticity in the cloud is often achieved by on-demand autoscaling. In such context, the goal is to optimize the Quality of Service (QoS) and cost objectives for the cloud-based services. However, the difficulty lies in the facts that these objectives, e.g., throughput and cost, can be naturally conflicted; and the QoS of cloud-based services often interfere due to the shared infrastructure in cloud. Consequently, dynamic and effective trade-off decision making of autoscaling in the cloud is necessary, yet challenging. In particular, it is even harder to achieve well-compromised trade-offs, where the decision largely improves the majority of the objectives; while causing relatively small degradations to others. In this paper, we present a self-adaptive decision making approach for autoscaling in the cloud. It is capable to adaptively produce autoscaling decisions that lead to well-compromised trade-offs without heavy human intervention. We leverage on ant colony inspired multi-objective optimization for searching and optimizing the trade-offs decisions, the result is then filtered by compromise-dominance, a mechanism that extracts the decisions with balanced improvements in the trade-offs. We experimentally compare our approach to four state-of-the-arts autoscaling approaches: rule, heuristic, randomized and multi-objective genetic algorithm based solutions. The results reveal the effectiveness of our approach over the others, including better quality of trade-offs and significantly smaller violation of the requirements.

Connected through hyperedge HE-1 (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"
    - "femosaa feature guided and knee driven multi objective optimization for self adaptive software"
    - "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-2 (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"
    - "femosaa feature guided and knee driven multi objective optimization for self adaptive software"
    - "efficient utility driven self healing employing adaptation rules for large dynamic architectures"
    - "privacy policy negotiation in social media"

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"
    - "transfer learning for improving model predictions in highly configurable software"
    - "daleel simplifying cloud instance selection using machine learning"

Connected through hyperedge HE-4 (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"
    - "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"
    - "the handbook of engineering self aware and self expressive systems"
    - "aneka a software platform for net based cloud computing"

Connected through hyperedge HE-5 (induced by source paper "femosaa feature guided and knee driven multi objective optimization for self adaptive software"):
  with members:
    - "online qos modeling in the cloud a hybrid and adaptive multi learners approach"

Connected through hyperedge HE-6 (induced by source paper "survey and taxonomy of self aware and self adaptive autoscaling systems in the cloud"):
  with members:
    - "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"
    - "the handbook of engineering self aware and self expressive systems"

Connected through hyperedge HE-7 (induced by source paper "auto scaling web applications in clouds a taxonomy and survey"):
  with members:
    - "a reliable and cost efficient auto scaling system for web applications using heterogeneous spot instances"
    - "dynamic selection of virtual machines for application servers in cloud environments"
    - "depas a decentralized probabilistic algorithm for auto scaling"

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.DC(Distributed, Parallel, and Cluster Computing)