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: "the missing piece in complex analytics low latency scalable model management and serving with velox"
  Abstract: To support complex data-intensive applications such as personalized recommendations, targeted advertising, and intelligent services, the data management community has focused heavily on the design of systems to support training complex models on large datasets. Unfortunately, the design of these systems largely ignores a critical component of the overall analytics process: the deployment and serving of models at scale. In this work, we present Velox, a new component of the Berkeley Data Analytics Stack. Velox is a data management system for facilitating the next steps in real-world, large-scale analytics pipelines: online model management, maintenance, and serving. Velox provides end-user applications and services with a low-latency, intuitive interface to models, transforming the raw statistical models currently trained using existing offline large-scale compute frameworks into full-blown, end-to-end data products capable of recommending products, targeting advertisements, and personalizing web content. To provide up-to-date results for these complex models, Velox also facilitates lightweight online model maintenance and selection (i.e., dynamic weighting). In this paper, we describe the challenges and architectural considerations required to achieve this functionality, including the abilities to span online and offline systems, to adaptively adjust model materialization strategies, and to exploit inherent statistical properties such as model error tolerance, all while operating at "Big Data" scale.

Connected through hyperedge HE-1 (induced by source paper "machine learning systems for highly distributed and rapidly growing data"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "semi cyclic stochastic gradient descent"
    - "the non iid data quagmire of decentralized machine learning"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "group normalization"
    - "federated learning with non iid data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (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-3 (induced by source paper "modipick sla aware accuracy optimization for mobile deep inference"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "tensorflow serving flexible high performance ml serving"
    - "deepasl enabling ubiquitous and non intrusive word and sentence level sign language translation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "cloud based or on device an empirical study of mobile deep inference"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-4 (induced by source paper "one size does not fit all quantifying and exposing the accuracy latency trade off in machine learning cloud service apis via tolerance tiers"):
  with members:
    - "branchynet fast inference via early exiting from deep neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "clipper a low latency online prediction serving system"
    - "the microsoft 2016 conversational speech recognition system"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "conditional deep learning for energy efficient and enhanced pattern recognition"

Connected through hyperedge HE-5 (induced by source paper "the machine learning bazaar harnessing the ml ecosystem for effective system development"):
  with members:
    - "neuronblocks building your nlp dnn models like playing lego"
    - "massively parallel hyperparameter tuning"
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "xgboost a scalable tree boosting system"
    - "cma es for hyperparameter optimization of deep neural networks"
    - "non stochastic best arm identification and hyperparameter optimization"

Connected through hyperedge HE-6 (induced by source paper "temporally biased sampling schemes for online model management"):
  with members:
    - "temporally biased sampling for online model management"
    - "macrobase prioritizing attention in fast data"
    - "weighted random sampling over data streams"
    - "stream sampling for variance optimal estimation of subset sums"

Connected through hyperedge HE-7 (induced by source paper "deepbase deep inspection of neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "learning to generate reviews and discovering sentiment"
    - "network dissection quantifying interpretability of deep visual representations"
    - "robustfill neural program learning under noisy i o"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"

Connected through hyperedge HE-8 (induced by source paper "pretzel opening the black box of machine learning prediction serving systems"):
  with members:
    - "tvm end to end optimization stack for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "tensorflow serving flexible high performance ml serving"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "an overview of gradient descent optimization algorithms"

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)