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: "active sampler light weight accelerator for complex data analytics at scale"
  Abstract: Recent years have witnessed amazing outcomes from "Big Models" trained by "Big Data". Most popular algorithms for model training are iterative. Due to the surging volumes of data, we can usually afford to process only a fraction of the training data in each iteration. Typically, the data are either uniformly sampled or sequentially accessed. #R##N#In this paper, we study how the data access pattern can affect model training. We propose an Active Sampler algorithm, where training data with more "learning value" to the model are sampled more frequently. The goal is to focus training effort on valuable instances near the classification boundaries, rather than evident cases, noisy data or outliers. We show the correctness and optimality of Active Sampler in theory, and then develop a light-weight vectorized implementation. Active Sampler is orthogonal to most approaches optimizing the efficiency of large-scale data analytics, and can be applied to most analytics models trained by stochastic gradient descent (SGD) algorithm. Extensive experimental evaluations demonstrate that Active Sampler can speed up the training procedure of SVM, feature selection and deep learning, for comparable training quality by 1.6-2.2x.

Connected through hyperedge HE-1 (induced by source paper "accelerating deep learning by focusing on the biggest losers"):
  with members:
    - "autoassist a framework to accelerate training of deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "not all samples are created equal deep learning with importance sampling"
    - "learning to reweight examples for robust deep learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "panda facilitating usable ai development"):
  with members:
    - "rafiki machine learning as an analytics service system"
    - "medical concept embedding with time aware attention"
    - "deep forest"
    - "forkbase an efficient storage engine for blockchain and forkable applications"
    - "progressive neural architecture search"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "the case for learned index structures"
    - "thoracic disease identification and localization with limited supervision"

Connected through hyperedge HE-3 (induced by source paper "database meets deep learning challenges and opportunities"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "deeper deep entity resolution"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "the case for learned index structures"
    - "an encoder decoder framework translating natural language to database queries"
    - "device placement optimization with reinforcement learning"
    - "memory efficient implementation of densenets"
    - "learning a neural semantic parser from user feedback"

Connected through hyperedge HE-4 (induced by source paper "active and continuous exploration with deep neural networks and expected model output changes"):
  with members:
    - "the unreasonable effectiveness of noisy data for fine grained recognition"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "bird species categorization using pose normalized deep convolutional nets"
    - "simultaneous detection and segmentation"
    - "neural codes for image retrieval"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)