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: "a comparison of reinforcement learning techniques for fuzzy cloud auto scaling"
  Abstract: A goal of cloud service management is to design self-adaptable auto-scaler to react to workload fluctuations and changing the resources assigned. The key problem is how and when to add/remove resources in order to meet agreed service-level agreements. Reducing application cost and guaranteeing service-level agreements (SLAs) are two critical factors of dynamic controller design. In this paper, we compare two dynamic learning strategies based on a fuzzy logic system, which learns and modifies fuzzy scaling rules at runtime. A self-adaptive fuzzy logic controller is combined with two reinforcement learning (RL) approaches: (i) Fuzzy SARSA learning (FSL) and (ii) Fuzzy Q-learning (FQL). As an off-policy approach, Q-learning learns independent of the policy currently followed, whereas SARSA as an on-policy always incorporates the actual agent's behavior and leads to faster learning. Both approaches are implemented and compared in their advantages and disadvantages, here in the OpenStack cloud platform. We demonstrate that both auto-scaling approaches can handle various load traffic situations, sudden and periodic, and delivering resources on demand while reducing operating costs and preventing SLA violations. The experimental results demonstrate that FSL and FQL have acceptable performance in terms of adjusted number of virtual machine targeted to optimize SLA compliance and response time.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning based autoscaling of workflows in the cloud a survey"):
  with members:
    - "cmi an online multi objective genetic autoscaler for scientific and engineering workflows in cloud infrastructures with unreliable virtual machines"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "deep learning a critical appraisal"
    - "automated cloud provisioning on aws using deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "a taxonomy and survey of energy efficient data centers and cloud computing systems"
    - "high performance cloud computing a view of scientific applications"

Connected through hyperedge HE-2 (induced by source paper "a view on deep reinforcement learning in system optimization"):
  with members:
    - "neural packet classification"
    - "neo a learned query optimizer"
    - "reinforced genetic algorithm learning for optimizing computation graphs"
    - "a survey on compiler autotuning using machine learning"
    - "placeto learning generalizable device placement algorithms for distributed machine learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "iroko a framework to prototype reinforcement learning for data center traffic control"
    - "seq2sql generating structured queries from natural language using reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "feature model guided online learning for self adaptive systems"):
  with members:
    - "transfer learning for improving model predictions in highly configurable software"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "research challenges in nextgen service orchestration"):
  with members:
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "managing service heterogeneity using osmotic computing"
    - "mobile cloud business process management system for the internet of things a survey"
    - "enorm a framework for edge node resource management"
    - "occupy the cloud distributed computing for the 99"
    - "on using micro clouds to deliver the fog"
    - "improving spark application throughput via memory aware task co location a mixture of experts approach"
    - "daleel simplifying cloud instance selection using machine learning"

Connected through hyperedge HE-5 (induced by source paper "adares adaptive resource management for virtual machines"):
  with members:
    - "deep reinforcement learning a brief survey"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "playing atari with deep reinforcement learning"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandit algorithms with supervised learning guarantees"

Connected through hyperedge HE-6 (induced by source paper "alvec auto scaling by lotka volterra elastic cloud a qos aware non linear dynamical allocation model"):
  with members:
    - "sustainable cloud computing foundations and future directions"
    - "qos guaranteed intelligent routing using hybrid pso ga in wireless mesh networks"
    - "scads scale independent storage for social computing applications"

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)