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: "online qos modeling in the cloud a hybrid and adaptive multi learners approach"
  Abstract: Given the on-demand nature of cloud computing, managing cloud-based services requires accurate modeling for the correlation between their Quality of Service (QoS) and cloud configurations/resources. The resulted models need to cope with the dynamic fluctuation of QoS sensitivity and interference. However, existing QoS modeling in the cloud are limited in terms of both accuracy and applicability due to their static and semi-dynamic nature. In this paper, we present a fully dynamic multi-learners approach for automated and online QoS modeling in the cloud. We contribute to a hybrid learners solution, which improves accuracy while keeping model complexity adequate. To determine the inputs of QoS model at runtime, we partition the inputs space into two sub-spaces, each of which applies different symmetric uncertainty based selection techniques, and we then combine the sub-spaces results. The learners are also adaptive, they simultaneously allow several machine learning algorithms to model QoS function and dynamically select the best model for prediction on the fly. We experimentally evaluate our models using RUBiS benchmark and realistic FIFA 98 workload. The results show that our multi-learners approach is more accurate and effective in contrast to the other state-of-the-art approaches.

Connected through hyperedge HE-1 (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"
    - "the handbook of engineering self aware and self expressive systems"
    - "aneka a software platform for net based cloud computing"

Connected through hyperedge HE-2 (induced by source paper "femosaa feature guided and knee driven multi objective optimization for self adaptive software"):
  with members:
    - "self adaptive trade off decision making for autoscaling cloud based services"

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"
    - "the handbook of engineering self aware and self expressive systems"

Connected through hyperedge HE-4 (induced by source paper "bridging ecology and cloud transposing ecological prespective to enable better cloud autoscaling"):
  with members:
    - "the handbook of engineering self aware and self expressive systems"
    - "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:
    - "the handbook of engineering self aware and self expressive systems"

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)