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: "idebench a benchmark for interactive data exploration"
  Abstract: Existing benchmarks for analytical database systems such as TPC-DS and TPC-H are designed for static reporting scenarios. The main metric of these benchmarks is the performance of running individual SQL queries over a synthetic database. In this paper, we argue that such benchmarks are not suitable for evaluating database workloads originating from interactive data exploration (IDE) systems where most queries are ad-hoc, not based on predefined reports, and built incrementally. As a main contribution, we present a novel benchmark called IDEBench that can be used to evaluate the performance of database systems for IDE workloads. As opposed to traditional benchmarks for analytical database systems, our goal is to provide more meaningful workloads and datasets that can be used to benchmark IDE query engines, with a particular focus on metrics that capture the trade-off between query performance and quality of the result. As a second contribution, this paper evaluates and discusses the performance results of selected IDE query engines using our benchmark. The study includes two commercial systems, as well as two research prototypes (IDEA, approXimateDB/XDB), and one traditional analytical database system (MonetDB).

Connected through hyperedge HE-1 (induced by source paper "sample debiasing in the themis open world database system extended version"):
  with members:
    - "probabilistic database summarization for interactive data exploration"
    - "database learning toward a database that becomes smarter every time"
    - "estimating the impact of unknown unknowns on aggregate query results"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "sample selection bias correction theory"

Connected through hyperedge HE-2 (induced by source paper "approximate query processing using deep generative models"):
  with members:
    - "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"
    - "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-3 (induced by source paper "mosaic a sample based database system for open world query processing"):
  with members:
    - "approximate query processing using deep generative models"
    - "neo a learned query optimizer"
    - "entropydb a probabilistic approach to approximate query processing"
    - "on constrained open world probabilistic databases"
    - "model based approximate query processing"
    - "generative modeling using the sliced wasserstein distance"
    - "database learning toward a database that becomes smarter every time"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "viznet towards a large scale visualization learning and benchmarking repository"):
  with members:
    - "beyond heuristics learning visualization design"
    - "vizml a machine learning approach to visualization recommendation"
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"
    - "task based effectiveness of basic visualizations"
    - "beagle automated extraction and interpretation of visualizations from the web"
    - "squad 100 000 questions for machine comprehension of text"
    - "boomerang rebounding the consequences of reputation feedback on crowdsourcing platforms"
    - "viziometrics analyzing visual information in the scientific literature"

Connected through hyperedge HE-5 (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"
    - "verdictdb universalizing approximate query processing"
    - "the case for learned index structures"

Connected through hyperedge HE-6 (induced by source paper "model based approximate query processing"):
  with members:
    - "database learning toward a database that becomes smarter every time"
    - "sum product networks a new deep architecture"
    - "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)