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: "verdictdb universalizing approximate query processing"
  Abstract: Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional vendors to make radical changes to their legacy codebases, and the preoccupation of newer vendors (e.g., SQL-on-Hadoop products) with implementing standard features. Additionally, the few AQP engines that are available are each tied to a specific platform and require users to completely abandon their existing databases---an unrealistic expectation given the infancy of the AQP technology. Therefore, we argue that a universal solution is needed: a database-agnostic approximation engine that will widen the reach of this emerging technology across various platforms.   Our proposal, called VerdictDB, uses a middleware architecture that requires no changes to the backend database, and thus, can work with all off-the-shelf engines. Operating at the driver-level, VerdictDB intercepts analytical queries issued to the database and rewrites them into another query that, if executed by any standard relational engine, will yield sufficient information for computing an approximate answer. VerdictDB uses the returned result set to compute an approximate answer and error estimates, which are then passed on to the user or application. However, lack of access to the query execution layer introduces significant challenges in terms of generality, correctness, and efficiency. This paper shows how VerdictDB overcomes these challenges and delivers up to 171× speedup (18.45× on average) for a variety of existing engines, such as Impala, Spark SQL, and Amazon Redshift, while incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache License.

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"
    - "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-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"
    - "neo a learned query optimizer"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "towards scalable dataframe systems"):
  with members:
    - "helix holistic optimization for accelerating iterative machine learning"
    - "ray a distributed framework for emerging ai applications"
    - "adaptive sampling for rapidly matching histograms"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "towards a holistic integration of spreadsheets with databases a scalable storage engine for presentational data management"
    - "lara a key value algebra underlying arrays and relations"
    - "visualization aware sampling for very large databases"
    - "rapid sampling for visualizations with ordering guarantees"

Connected through hyperedge HE-4 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "measuring the effects of data parallelism on neural network training"
    - "hemingway modeling distributed optimization algorithms"
    - "unbiased estimates for linear regression via volume sampling"
    - "database learning toward a database that becomes smarter every time"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"

Connected through hyperedge HE-5 (induced by source paper "physical representation based predicate optimization for a visual analytics database"):
  with members:
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "blazeit fast exploratory video queries using neural networks"
    - "focus querying large video datasets with low latency and low cost"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "noscope optimizing neural network queries over video at scale"
    - "macrobase prioritizing attention in fast data"

Connected through hyperedge HE-6 (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"
    - "deep reinforcement learning for join order enumeration"
    - "data synthesis based on generative adversarial networks"
    - "learning to optimize join queries with deep reinforcement learning"
    - "neuralcubes deep representations for visual data exploration"
    - "model based approximate query processing"

Connected through hyperedge HE-7 (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"
    - "multi attribute selectivity estimation using deep learning"
    - "an end to end learning based cost estimator"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "idebench a benchmark for interactive data exploration"
    - "the case for learned index structures"

Connected through hyperedge HE-8 (induced by source paper "entropydb a probabilistic approach to approximate query processing"):
  with members:
    - "approximate query processing using deep generative models"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "probabilistic database summarization for interactive data exploration"
    - "generalization in adaptive data analysis and holdout reuse"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

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)