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: "sampling based query re optimization"
  Abstract: Despite of decades of work, query optimizers still make mistakes on "difficult" queries because of bad cardinality estimates, often due to the interaction of multiple predicates and correlations in the data. In this paper, we propose a low-cost post-processing step that can take a plan produced by the optimizer, detect when it is likely to have made such a mistake, and take steps to fix it. Specifically, our solution is a sampling-based iterative procedure that requires almost no changes to the original query optimizer or query evaluation mechanism of the system. We show that this indeed imposes low overhead and catches cases where three widely used optimizers (PostgreSQL and two commercial systems) make large errors.

Connected through hyperedge HE-1 (induced by source paper "a note on operator level query execution cost modeling"):
  with members:
    - "robust estimation of resource consumption for sql queries using statistical techniques"

Connected through hyperedge HE-2 (induced by source paper "an empirical analysis of deep learning for cardinality estimation"):
  with members:
    - "multi attribute selectivity estimation using deep learning"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "learning to optimize join queries with deep reinforcement learning"
    - "state representation learning for control an overview"
    - "towards a hands free query optimizer through deep learning"
    - "pool based sequential active learning for regression"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

Connected through hyperedge HE-3 (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"
    - "ordering selection operators using the minmax regret rule"
    - "bandit algorithms for tree search"
    - "simple regret optimization in online planning for markov decision processes"
    - "robust estimation of resource consumption for sql queries using statistical techniques"
    - "leapfrog triejoin a worst case optimal join algorithm"
    - "worst case optimal join algorithms"

Connected through hyperedge HE-4 (induced by source paper "an end to end learning based cost estimator"):
  with members:
    - "plan structured deep neural network models for query performance prediction"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "forecasting the cost of processing multi join queries via hashing for main memory databases"
    - "distributed representations of words and phrases and their compositionality"
    - "robust estimation of resource consumption for sql queries using statistical techniques"

Connected through hyperedge HE-5 (induced by source paper "joins on samples a theoretical guide for practitioners"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "verdictdb universalizing approximate query processing"
    - "quicksel quick selectivity learning with mixture models"
    - "size bounds and query plans for relational joins"
    - "visualization aware sampling for very large databases"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-6 (induced by source paper "learned cardinalities estimating correlated joins with deep learning"):
  with members:
    - "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"
    - "deep sets"
    - "on calibration of modern neural networks"
    - "the case for learned index structures"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-7 (induced by source paper "progressive evaluation of queries over untagged data"):
  with members:
    - "an idea an ingestion framework for data enrichment in asterixdb"
    - "verdictdb universalizing approximate query processing"
    - "meta des"
    - "exploiting correlations for expensive predicate evaluation"

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)