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: "robust estimation of resource consumption for sql queries using statistical techniques"
  Abstract: The ability to estimate resource consumption of SQL queries is crucial for a number of tasks in a database system such as admission control, query scheduling and costing during query optimization. Recent work has explored the use of statistical techniques for resource estimation in place of the manually constructed cost models used in query optimization. Such techniques, which require as training data examples of resource usage in queries, offer the promise of superior estimation accuracy since they can account for factors such as hardware characteristics of the system or bias in cardinality estimates. However, the proposed approaches lack robustness in that they do not generalize well to queries that are different from the training examples, resulting in significant estimation errors. Our approach aims to address this problem by combining knowledge of database query processing with statistical models. We model resource-usage at the level of individual operators, with different models and features for each operator type, and explicitly model the asymptotic behavior of each operator. This results in significantly better estimation accuracy and the ability to estimate resource usage of arbitrary plans, even when they are very different from the training instances. We validate our approach using various large scale real-life and benchmark workloads on Microsoft SQL Server.

Connected through hyperedge HE-1 (induced by source paper "facilitating sql query composition and analysis"):
  with members:
    - "query2vec nlp meets databases for generalized workload analytics"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a survey of machine learning for big code and naturalness"
    - "comparative study of cnn and rnn for natural language processing"
    - "recent trends in deep learning based natural language processing"
    - "convolutional neural networks for text categorization shallow word level vs deep character level"
    - "very deep convolutional networks for text classification"
    - "database meets deep learning challenges and opportunities"

Connected through hyperedge HE-2 (induced by source paper "cost models for big data query processing learning retrofitting and our findings"):
  with members:
    - "neo a learned query optimizer"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "helix holistic optimization for accelerating iterative machine learning"
    - "the case for learned index structures"

Connected through hyperedge HE-3 (induced by source paper "a note on operator level query execution cost modeling"):
  with members:
    - "sampling based query re optimization"

Connected through hyperedge HE-4 (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-5 (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"
    - "wisedb a learning based workload management advisor for cloud databases"
    - "database meets deep learning challenges and opportunities"
    - "a critical review of recurrent neural networks for sequence learning"

Connected through hyperedge HE-6 (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-7 (induced by source paper "improved cardinality estimation by learning queries containment rates"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "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"
    - "activation functions comparison of trends in practice and research for deep learning"
    - "the case for learned index structures"
    - "overcoming catastrophic forgetting in neural networks"
    - "a fast unified model for parsing and sentence understanding"

Connected through hyperedge HE-8 (induced by source paper "skinnerdb regret bounded query evaluation via reinforcement learning"):
  with members:
    - "deep reinforcement learning for join order enumeration"
    - "learning to optimize join queries with deep reinforcement learning"
    - "sampling based query re optimization"
    - "ordering selection operators using the minmax regret rule"
    - "bandit algorithms for tree search"
    - "simple regret optimization in online planning for markov decision processes"
    - "leapfrog triejoin a worst case optimal join algorithm"
    - "worst case optimal join algorithms"

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)