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: "database learning toward a database that becomes smarter every time"
  Abstract: In today's databases, previous query answers rarely benefit answering future queries. For the first time, to the best of our knowledge, we change this paradigm in an approximate query processing (AQP) context. We make the following observation: the answer to each query reveals some degree of knowledge about the answer to another query because their answers stem from the same underlying distribution that has produced the entire dataset. Exploiting and refining this knowledge should allow us to answer queries more analytically, rather than by reading enormous amounts of raw data. Also, processing more queries should continuously enhance our knowledge of the underlying distribution, and hence lead to increasingly faster response times for future queries.   We call this novel idea---learning from past query answers---Database Learning. We exploit the principle of maximum entropy to produce answers, which are in expectation guaranteed to be more accurate than existing sample-based approximations. Empowered by this idea, we build a query engine on top of Spark SQL, called Verdict. We conduct extensive experiments on real-world query traces from a large customer of a major database vendor. Our results demonstrate that database learning supports 73.7% of these queries, speeding them up by up to 23.0x for the same accuracy level compared to existing AQP systems.

Connected through hyperedge HE-1 (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-2 (induced by source paper "sample debiasing in the themis open world database system extended version"):
  with members:
    - "idebench a benchmark for interactive data exploration"
    - "probabilistic database summarization for interactive data exploration"
    - "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-3 (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"
    - "verdictdb universalizing approximate query processing"
    - "measuring the effects of data parallelism on neural network training"
    - "hemingway modeling distributed optimization algorithms"
    - "unbiased estimates for linear regression via volume sampling"
    - "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-4 (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"
    - "idebench a benchmark for interactive data exploration"
    - "model based approximate query processing"
    - "generative modeling using the sliced wasserstein distance"
    - "nips 2016 tutorial generative adversarial networks"

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"
    - "idebench a benchmark for interactive data exploration"
    - "verdictdb universalizing approximate query processing"

Connected through hyperedge HE-6 (induced by source paper "query the model precomputations for efficient inference with bayesian networks"):
  with members:
    - "a simple cache model for image recognition"
    - "generative neural machine translation"
    - "d separation from theorems to algorithms"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-7 (induced by source paper "adaptive learning of aggregate analytics under dynamic workloads"):
  with members:
    - "verdictdb universalizing approximate query processing"
    - "xgboost a scalable tree boosting system"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-8 (induced by source paper "explaining aggregates for exploratory analytics"):
  with members:
    - "foresight recommending visual insights"
    - "axiomatic attribution for deep networks"
    - "macrobase prioritizing attention in fast data"
    - "macrobase analytic monitoring for the internet of things"
    - "why should i trust you explaining the predictions of any classifier"
    - "qfix diagnosing errors through query histories"
    - "perfxplain debugging mapreduce job performance"
    - "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)