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: "wisedb a learning based workload management advisor for cloud databases"
  Abstract: Workload management for cloud databases deals with the tasks of resource provisioning, query placement, and query scheduling in a manner that meets the application's performance goals while minimizing the cost of using cloud resources. Existing solutions have approached these three challenges in isolation while aiming to optimize a single performance metric. In this paper, we introduce WiSeDB, a learning-based framework for generating holistic workload management solutions customized to application-defined performance goals and workload characteristics. Our approach relies on supervised learning to train cost-effective decision tree models for guiding query placement, scheduling, and resource provisioning decisions. Applications can use these models for both batch and online scheduling of incoming workloads. A unique feature of our system is that it can adapt its offline model to stricter/looser performance goals with minimal re-training. This allows us to present to the application alternative workload management strategies that address the typical performance vs. cost trade-off of cloud services. Experimental results show that our approach has very low training overhead while offering low cost strategies for a variety of performance metrics and workload characteristics.

Connected through hyperedge HE-1 (induced by source paper "flexible operator embeddings via deep learning"):
  with members:
    - "termite a system for tunneling through heterogeneous data"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "deep reinforcement learning for join order enumeration"
    - "database agnostic workload management"
    - "deep contextualized word representations"
    - "helix accelerating human in the loop machine learning"
    - "towards a hands free query optimizer through deep learning"
    - "the case for learned index structures"

Connected through hyperedge HE-2 (induced by source paper "plan structured deep neural network models for query performance prediction"):
  with members:
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "lift reinforcement learning in computer systems by learning from demonstrations"
    - "the case for learned index structures"
    - "attention is all you need"
    - "database meets deep learning challenges and opportunities"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep learning in neural networks"

Connected through hyperedge HE-3 (induced by source paper "join query optimization with deep reinforcement learning algorithms"):
  with members:
    - "neo a learned query optimizer"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "learning to optimize join queries with deep reinforcement learning"
    - "modular architecture for starcraft ii with deep reinforcement learning"
    - "towards a hands free query optimizer through deep learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "a consumer centric market for database computation in the cloud"):
  with members:
    - "perfenforce a dynamic scaling engine for analytics with performance guarantees"
    - "uncertainty aware query execution time prediction"
    - "mathematical frameworks for pricing in the cloud revenue fairness and resource allocations"
    - "robust estimation of resource consumption for sql queries using statistical techniques"

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.DB(Databases)