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: "towards a hands free query optimizer through deep learning"
  Abstract: Query optimization remains one of the most important and well-studied problems in database systems. However, traditional query optimizers are complex heuristically-driven systems, requiring large amounts of time to tune for a particular database and requiring even more time to develop and maintain in the first place. In this vision paper, we argue that a new type of query optimizer, based on deep reinforcement learning, can drastically improve on the state-of-the-art. We identify potential complications for future research that integrates deep learning with query optimization, and we describe three novel deep learning based approaches that can lead the way to end-to-end learning-based query optimizers.

Connected through hyperedge HE-1 (induced by source paper "video monitoring queries"):
  with members:
    - "approximate query processing using deep generative models"
    - "multi attribute selectivity estimation using deep learning"
    - "yolov3 an incremental improvement"
    - "blazeit fast exploratory video queries using neural networks"
    - "verdictdb universalizing approximate query processing"
    - "focus querying large video datasets with low latency and low cost"
    - "the case for learned index structures"
    - "weakly supervised object localization with multi fold multiple instance 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"
    - "deep reinforcement learning for join order enumeration"
    - "learning scheduling algorithms for data processing clusters"
    - "learning to optimize join queries with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learning index selection with structured action spaces"):
  with members:
    - "learning action representations for reinforcement learning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "the case for automatic database administration using deep reinforcement learning"
    - "darts differentiable architecture search"
    - "lift reinforcement learning in computer systems by learning from demonstrations"
    - "learning permutations with sinkhorn policy gradient"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-4 (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"
    - "pool based sequential active learning for regression"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "the case for learned index structures"

Connected through hyperedge HE-5 (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"
    - "the case for learned index structures"
    - "wisedb a learning based workload management advisor for cloud databases"

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"
    - "rainbow combining improvements in deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "wisedb a learning based workload management advisor for cloud databases"

Connected through hyperedge HE-7 (induced by source paper "estimating cardinalities with deep sketches"):
  with members:
    - "opportunistic view materialization with deep reinforcement learning"
    - "skinnerdb regret bounded query evaluation via reinforcement learning"
    - "plan structured deep neural network models for query performance prediction"
    - "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"
    - "deep reinforcement learning for join order enumeration"
    - "database agnostic workload management"

Connected through hyperedge HE-8 (induced by source paper "alex an updatable adaptive learned index"):
  with members:
    - "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"
    - "learning memory access patterns"
    - "a tree a bounded approximate index structure"
    - "the case for learned index structures"
    - "insertion sort is o n log n"

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)