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: "asap prioritizing attention via time series smoothing"
  Abstract: Time series visualization of streaming telemetry (i.e., charting of key metrics such as server load over time) is increasingly prevalent in modern data platforms and applications. However, many existing systems simply plot the raw data streams as they arrive, often obscuring large-scale trends due to small-scale noise. We propose an alternative: to better prioritize end users' attention, smooth time series visualizations as much as possible to remove noise, while retaining large-scale structure to highlight significant deviations. We develop a new analytics operator called ASAP that automatically smooths streaming time series by adaptively optimizing the trade-off between noise reduction (i.e., variance) and trend retention (i.e., kurtosis). We introduce metrics to quantitatively assess the quality of smoothed plots and provide an efficient search strategy for optimizing these metrics that combines techniques from stream processing, user interface design, and signal processing via autocorrelation-based pruning, pixel-aware preaggregation, and on-demand refresh. We demonstrate that ASAP can improve users' accuracy in identifying long-term deviations in time series by up to 38.4% while reducing response times by up to 44.3%. Moreover, ASAP delivers these results several orders of magnitude faster than alternative search strategies.

Connected through hyperedge HE-1 (induced by source paper "towards a near universal time series data mining tool introducing the matrix profile"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "learning from hidden traits joint factor analysis and latent clustering"
    - "m tsne a framework for visualizing high dimensional multivariate time series"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for recommendation"
    - "distributed and parallel time series feature extraction for industrial big data applications"
    - "node2vec scalable feature learning for networks"
    - "adversarial feature learning"

Connected through hyperedge HE-2 (induced by source paper "at a glance pixel approximate entropy as a measure of line chart complexity"):
  with members:
    - "entropies from markov models as complexity measures of embedded attractors"

Connected through hyperedge HE-3 (induced by source paper "infrastructure for usable machine learning the stanford dawn project"):
  with members:
    - "learning the structure of generative models without labeled data"
    - "noscope optimizing neural network queries over video at scale"
    - "macrobase prioritizing attention in fast data"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"
    - "socratic learning augmenting generative models to incorporate latent subsets in training data"
    - "a two pronged progress in structured dense matrix multiplication"
    - "slimfast guaranteed results for data fusion and source reliability"
    - "worst case optimal join algorithms"

Connected through hyperedge HE-4 (induced by source paper "macrobase prioritizing attention in fast data"):
  with members:
    - "sketching linear classifiers over data streams"
    - "drop dimensionality reduction optimization for time series"
    - "noscope optimizing neural network queries over video at scale"
    - "building efficient query engines in a high level language"
    - "incremental knowledge base construction using deepdive"
    - "graph based anomaly detection and description a survey"
    - "towards a unified architecture for in rdbms analytics"
    - "identifying the signs of fraudulent accounts using data mining techniques"

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)