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: "using application data for sla aware auto scaling in cloud environments"
  Abstract: With the establishment of cloud computing as the environment of choice for most modern applications, auto-scaling is an economic matter of great importance. For applications like stream computing that process ever changing amounts of data, modifying the number and configuration of resources to meet performance requirements becomes essential. Current solutions on auto-scaling are mostly rule-based using infrastructure level metrics such as CPU/memory/network utilization, and system level metrics such as throughput and response time. In this paper, we introduce a study on how effective auto-scaling can be using data generated by the application itself. To make this assessment, two algorithms are proposed that use a priori knowledge of the data stream and use sentiment analysis from soccer-related tweets, triggering auto-scaling operations according to rapid changes in the public sentiment about the soccer players that happens just before big bursts of messages. Our application-based auto-scaling was able to reduce the number of SLA violations by up to 95% and reduce resource requirements by up to 33%.

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"
    - "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-2 (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"
    - "online qos modeling in the cloud a hybrid and adaptive multi learners approach"
    - "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)