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: "deep reinforcement learning for join order enumeration"
  Abstract: Join order selection plays a significant role in query performance. However, modern query optimizers typically employ static join order enumeration algorithms that do not incorporate feedback about the quality of the resulting plan. Hence, optimizers often repeatedly choose the same bad plan, as they have no mechanism for "learning from their mistakes." Here, we argue that deep reinforcement learning techniques can be applied to address this challenge. These techniques, powered by artificial neural networks, can automatically improve optimizer decision-making by incorporating feedback. Towards this goal, we present ReJOIN, a proof-of-concept join enumerator, as well as preliminary results indicating that ReJOIN can match or outperform the PostgreSQL optimizer in terms of plan quality and join enumeration efficiency.

Connected through hyperedge HE-1 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach"):
  with members:
    - "neo a learned query optimizer"
    - "selectivity estimation with deep likelihood models"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "an empirical analysis of deep learning for cardinality estimation"
    - "multi attribute selectivity estimation using deep learning"
    - "an end to end learning based cost estimator"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "neo a learned query optimizer"):
  with members:
    - "termite a system for tunneling through heterogeneous data"
    - "skinnerdb regret bounded query evaluation via reinforcement learning"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "learning scheduling algorithms for data processing clusters"
    - "learning to optimize join queries with deep reinforcement learning"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "learned indexes for dynamic workloads"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "regularized evolution for image classifier architecture search"
    - "learning to optimize join queries with deep reinforcement learning"
    - "learning memory access patterns"
    - "quicksel quick selectivity learning with mixture models"
    - "an o n sorting algorithm machine learning sorting"

Connected through hyperedge HE-4 (induced by source paper "wield systematic reinforcement learning with progressive randomization"):
  with members:
    - "neo a learned query optimizer"
    - "challenges of real world reinforcement learning"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "learning scheduling algorithms for data processing clusters"
    - "rlgraph modular computation graphs for deep reinforcement learning"
    - "unity a general platform for intelligent agents"

Connected through hyperedge HE-5 (induced by source paper "approximate query processing using deep generative models"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "idebench a benchmark for interactive data exploration"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "data synthesis based on generative adversarial networks"
    - "learning to optimize join queries with deep reinforcement learning"
    - "neuralcubes deep representations for visual data exploration"
    - "verdictdb universalizing approximate query processing"
    - "model based approximate query processing"

Connected through hyperedge HE-6 (induced by source paper "selectivity estimation with deep likelihood models"):
  with members:
    - "neo a learned query optimizer"
    - "holodetect few shot learning for error detection"
    - "multi attribute selectivity estimation using deep learning"
    - "how i learned to stop worrying and love re optimization"
    - "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"
    - "quicksel quick selectivity learning with mixture models"

Connected through hyperedge HE-7 (induced by source paper "deep unsupervised cardinality estimation"):
  with members:
    - "neo a learned query optimizer"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "how i learned to stop worrying and love re optimization"
    - "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"
    - "quicksel quick selectivity learning with mixture models"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (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"
    - "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"
    - "wisedb a learning based workload management advisor for cloud databases"

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)