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: "quicksel quick selectivity learning with mixture models"
  Abstract: Estimating the selectivity of a query is a key step in almost any cost-based query optimizer. Most of today's databases rely on histograms or samples that are periodically refreshed by re-scanning the data as the underlying data changes. Since frequent scans are costly, these statistics are often stale and lead to poor selectivity estimates. As an alternative to scans, query-driven histograms have been proposed, which refine the histograms based on the actual selectivities of the observed queries. Unfortunately, these approaches are either too costly to use in practice---i.e., require an exponential number of buckets---or quickly lose their advantage as they observe more queries. For example, the state-of-the-art technique requires 318,936 buckets (and over 8 seconds of refinement overhead per query) after observing only 300 queries. #R##N#In this paper, we propose a selectivity learning framework, called QuickSel, which falls into the query-driven paradigm but does not use histograms. Instead, it builds an internal model of the underlying data, which can be refined significantly faster (e.g., only 1.9 milliseconds for 300 queries). This fast refinement allows QuickSel to continuously learn from each query and yield increasingly more accurate selectivity estimates over time. Unlike query-driven histograms, QuickSel relies on a mixture model and a new optimization algorithm for training its model. Our extensive experiments on two real-world datasets confirm that, given the same target accuracy, QuickSel is on average 254.6x faster than state-of-the-art query-driven histograms, including ISOMER and STHoles. Further, given the same space budget, QuickSel is on average 57.3% and 91.1% more accurate than periodically-updated histograms and samples, respectively.

Connected through hyperedge HE-1 (induced by source paper "polyfit polynomial based indexing approach for fast approximate range aggregate queries"):
  with members:
    - "the case for learned index structures"

Connected through hyperedge HE-2 (induced by source paper "neo a learned query optimizer"):
  with members:
    - "termite a system for tunneling through heterogeneous data"
    - "skinnerdb regret bounded query evaluation via reinforcement learning"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "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 scheduling algorithms for data processing clusters"
    - "learning to optimize join queries with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learned indexes for dynamic workloads"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "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"
    - "regularized evolution for image classifier architecture search"
    - "learning to optimize join queries with deep reinforcement learning"
    - "learning memory access patterns"
    - "an o n sorting algorithm machine learning sorting"

Connected through hyperedge HE-4 (induced by source paper "selectivity estimation with deep likelihood models"):
  with members:
    - "neo a learned query optimizer"
    - "holodetect few shot learning for error detection"
    - "multi attribute selectivity estimation using deep learning"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "deep unsupervised cardinality estimation"):
  with members:
    - "neo a learned query optimizer"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "joins on samples a theoretical guide for practitioners"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "verdictdb universalizing approximate query processing"
    - "size bounds and query plans for relational joins"
    - "sampling based query re optimization"
    - "visualization aware sampling for very large databases"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-7 (induced by source paper "learning multi dimensional indexes"):
  with members:
    - "alex an updatable adaptive learned index"
    - "a tree a bounded approximate index structure"
    - "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)