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: "multi attribute selectivity estimation using deep learning"
  Abstract: Selectivity estimation - the problem of estimating the result size of queries - is a fundamental problem in databases. Accurate estimation of query selectivity involving multiple correlated attributes is especially challenging. Poor cardinality estimates could result in the selection of bad plans by the query optimizer. We investigate the feasibility of using deep learning based approaches for both point and range queries and propose two complementary approaches. Our first approach considers selectivity as an unsupervised deep density estimation problem. We successfully introduce techniques from neural density estimation for this purpose. The key idea is to decompose the joint distribution into a set of tractable conditional probability distributions such that they satisfy the autoregressive property. Our second approach formulates selectivity estimation as a supervised deep learning problem that predicts the selectivity of a given query. We also introduce and address a number of practical challenges arising when adapting deep learning for relational data. These include query/data featurization, incorporating query workload information in a deep learning framework and the dynamic scenario where both data and workload queries could be updated. Our extensive experiments with a special emphasis on queries with a large number of predicates and/or small result sizes demonstrates that our proposed techniques provide fast and accurate selective estimates with minimal space overhead.

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"
    - "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 "video monitoring queries"):
  with members:
    - "approximate query processing using deep generative models"
    - "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"
    - "towards a hands free query optimizer through deep learning"
    - "the case for learned index structures"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-3 (induced by source paper "selectivity estimation with deep likelihood models"):
  with members:
    - "neo a learned query optimizer"
    - "holodetect few shot learning for error detection"
    - "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-4 (induced by source paper "an empirical analysis of deep learning for cardinality estimation"):
  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"
    - "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"
    - "the case for learned index structures"

Connected through hyperedge HE-5 (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"
    - "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"
    - "learning to optimize join queries with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "deepdb learn from data not from queries"):
  with members:
    - "approximate query processing using deep generative models"
    - "neo a learned query optimizer"
    - "spflow an easy and extensible library for deep probabilistic learning using sum product networks"
    - "an end to end learning based cost estimator"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "idebench a benchmark for interactive data exploration"
    - "verdictdb universalizing approximate query processing"
    - "the case for learned index structures"

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)