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: "macrobase prioritizing attention in fast data"
  Abstract: As data volumes continue to rise, manual inspection is becoming increasingly untenable. In response, we present MacroBase, a data analytics engine that prioritizes end-user attention in high-volume fast data streams. MacroBase enables efficient, accurate, and modular analyses that highlight and aggregate important and unusual behavior, acting as a search engine for fast data. MacroBase is able to deliver order-of-magnitude speedups over alternatives by optimizing the combination of explanation and classification tasks and by leveraging a new reservoir sampler and heavy-hitters sketch specialized for fast data streams. As a result, MacroBase delivers accurate results at speeds of up to 2M events per second per query on a single core. The system has delivered meaningful results in production, including at a telematics company monitoring hundreds of thousands of vehicles.

Connected through hyperedge HE-1 (induced by source paper "storyboard optimizing precomputed summaries for aggregation"):
  with members:
    - "hillview a trillion cell spreadsheet for big data"
    - "ddsketch a fast and fully mergeable quantile sketch with relative error guarantees"
    - "moment based quantile sketches for efficient high cardinality aggregation queries"
    - "optimal quantile approximation in streams"
    - "coresets and sketches"
    - "structure aware sampling flexible and accurate summarization"
    - "unbiased matrix rounding"

Connected through hyperedge HE-2 (induced by source paper "automated machine learning state of the art and open challenges"):
  with members:
    - "learning internal representations"
    - "meta learning a survey"
    - "autostacker a compositional evolutionary learning system"
    - "regularized evolution for image classifier architecture search"
    - "rafiki machine learning as an analytics service system"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"

Connected through hyperedge HE-3 (induced by source paper "physical representation based predicate optimization for a visual analytics database"):
  with members:
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "blazeit fast exploratory video queries using neural networks"
    - "verdictdb universalizing approximate query processing"
    - "focus querying large video datasets with low latency and low cost"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "noscope optimizing neural network queries over video at scale"

Connected through hyperedge HE-4 (induced by source paper "alphaclean automatic generation of data cleaning pipelines"):
  with members:
    - "pyod a python toolbox for scalable outlier detection"
    - "tune a research platform for distributed model selection and training"
    - "holoclean holistic data repairs with probabilistic inference"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "a data quality metric dqm how to estimate the number of undetected errors in data sets"
    - "keystoneml optimizing pipelines for large scale advanced analytics"

Connected through hyperedge HE-5 (induced by source paper "temporally biased sampling schemes for online model management"):
  with members:
    - "temporally biased sampling for online model management"
    - "the missing piece in complex analytics low latency scalable model management and serving with velox"
    - "weighted random sampling over data streams"
    - "stream sampling for variance optimal estimation of subset sums"

Connected through hyperedge HE-6 (induced by source paper "debugging machine learning pipelines"):
  with members:
    - "data polygamy the many many relationships among urban spatio temporal data sets"
    - "qfix diagnosing errors through query histories"
    - "programing implementation of the quine mccluskey method for minimization of boolean expression"

Connected through hyperedge HE-7 (induced by source paper "explainit a declarative root cause analysis engine for time series data"):
  with members:
    - "a theoretical analysis of ndcg type ranking measures"

Connected through hyperedge HE-8 (induced by source paper "focus querying large video datasets with low latency and low cost"):
  with members:
    - "yolo9000 better faster stronger"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "compressing neural networks with the hashing trick"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning complexity aware cascades for deep pedestrian detection"

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)