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: "neo a learned query optimizer"
  Abstract: Query optimization is one of the most challenging problems in database systems. Despite the progress made over the past decades, query optimizers remain extremely complex components that require a great deal of hand-tuning for specific workloads and datasets. Motivated by this shortcoming and inspired by recent advances in applying machine learning to data management challenges, we introduce Neo (Neural Optimizer), a novel learning-based query optimizer that relies on deep neural networks to generate query executions plans. Neo bootstraps its query optimization model from existing optimizers and continues to learn from incoming queries, building upon its successes and learning from its failures. Furthermore, Neo naturally adapts to underlying data patterns and is robust to estimation errors. Experimental results demonstrate that Neo, even when bootstrapped from a simple optimizer like PostgreSQL, can learn a model that offers similar performance to state-of-the-art commercial optimizers, and in some cases even surpass them.

Connected through hyperedge HE-1 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach"):
  with members:
    - "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"
    - "deep reinforcement learning for join order enumeration"

Connected through hyperedge HE-2 (induced by source paper "ml aqp query driven approximate query processing based on machine learning"):
  with members:
    - "approximate query processing using deep generative models"
    - "selectivity estimation with deep likelihood models"
    - "deepdb learn from data not from queries"
    - "an empirical analysis of deep learning for cardinality estimation"
    - "an end to end learning based cost estimator"
    - "estimating cardinalities with deep sketches"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "neuralcubes deep representations for visual data exploration"

Connected through hyperedge HE-3 (induced by source paper "black or white how to develop an autotuner for memory based analytics extended version"):
  with members:
    - "learning based automatic parameter tuning for big data analytics frameworks"
    - "don t cry over spilled records memory elasticity of data parallel applications and its application to cluster scheduling"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"
    - "tempo robust and self tuning resource management in multi tenant parallel databases"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "cost models for big data query processing learning retrofitting and our findings"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "helix holistic optimization for accelerating iterative machine learning"
    - "the case for learned index structures"
    - "robust estimation of resource consumption for sql queries using statistical techniques"

Connected through hyperedge HE-5 (induced by source paper "a view on deep reinforcement learning in system optimization"):
  with members:
    - "neural packet classification"
    - "reinforced genetic algorithm learning for optimizing computation graphs"
    - "a survey on compiler autotuning using machine learning"
    - "placeto learning generalizable device placement algorithms for distributed machine learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "iroko a framework to prototype reinforcement learning for data center traffic control"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "learning state representations for query optimization with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "wield systematic reinforcement learning with progressive randomization"):
  with members:
    - "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"
    - "deep reinforcement learning for join order enumeration"
    - "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-7 (induced by source paper "selectivity estimation with deep likelihood models"):
  with members:
    - "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"
    - "deep reinforcement learning for join order enumeration"
    - "learning to optimize join queries with deep reinforcement learning"
    - "quicksel quick selectivity learning with mixture models"

Connected through hyperedge HE-8 (induced by source paper "deep unsupervised cardinality estimation"):
  with members:
    - "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"
    - "deep reinforcement learning for join order enumeration"
    - "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"

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)