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: "probabilistic database summarization for interactive data exploration"
  Abstract: We present a probabilistic approach to generate a small, query-able summary of a dataset for interactive data exploration. Departing from traditional summarization techniques, we use the Principle of Maximum Entropy to generate a probabilistic representation of the data that can be used to give approximate query answers. We develop the theoretical framework and formulation of our probabilistic representation and show how to use it to answer queries. We then present solving techniques and give three critical optimizations to improve preprocessing time and query accuracy. Lastly, we experimentally evaluate our work using a 5 GB dataset of flights within the United States and a 210 GB dataset from an astronomy particle simulation. While our current work only supports linear queries, we show that our technique can successfully answer queries faster than sampling while introducing, on average, no more error than sampling and can better distinguish between rare and nonexistent values.

Connected through hyperedge HE-1 (induced by source paper "sample debiasing in the themis open world database system extended version"):
  with members:
    - "idebench a benchmark 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 "dataset search a survey"):
  with members:
    - "interactive summarization and exploration of top aggregate query answers"
    - "ad hoc table retrieval using semantic similarity"
    - "cuttlefish a lightweight primitive for adaptive query processing"
    - "on the fly table generation"
    - "everything you always wanted to know about a dataset studies in data summarisation"
    - "a survey on data collection for machine learning a big data ai integration perspective"
    - "neural vector spaces for unsupervised information retrieval"
    - "entitables smart assistance for entity focused tables"

Connected through hyperedge HE-3 (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"
    - "verdictdb universalizing approximate query processing"
    - "generalization in adaptive data analysis and holdout reuse"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-4 (induced by source paper "reproducible floating point aggregation in rdbmss"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "the zipml framework for training models with end to end low precision the cans the cannots and a little bit of deep learning"

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)