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: "tupaq an efficient planner for large scale predictive analytic queries"
  Abstract: The proliferation of massive datasets combined with the development of sophisticated analytical techniques have enabled a wide variety of novel applications such as improved product recommendations, automatic image tagging, and improved speech-driven interfaces. These and many other applications can be supported by Predictive Analytic Queries (PAQs). A major obstacle to supporting PAQs is the challenging and expensive process of identifying and training an appropriate predictive model. Recent efforts aiming to automate this process have focused on single node implementations and have assumed that model training itself is a black box, thus limiting the effectiveness of such approaches on large-scale problems. In this work, we build upon these recent efforts and propose an integrated PAQ planning architecture that combines advanced model search techniques, bandit resource allocation via runtime algorithm introspection, and physical optimization via batching. The result is TuPAQ, a component of the MLbase system, which solves the PAQ planning problem with comparable quality to exhaustive strategies but an order of magnitude more efficiently than the standard baseline approach, and can scale to models trained on terabytes of data across hundreds of machines.

Connected through hyperedge HE-1 (induced by source paper "hyper parameter optimization a review of algorithms and applications"):
  with members:
    - "a generalized framework for population based training"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "meta learning a survey"
    - "deep learning using rectified linear units relu"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "massively parallel hyperparameter tuning"
    - "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (induced by source paper "accelerating human in the loop machine learning challenges and opportunities"):
  with members:
    - "helix holistic optimization for accelerating iterative machine learning"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-3 (induced by source paper "helix accelerating human in the loop machine learning"):
  with members:
    - "helix holistic optimization for accelerating iterative machine learning"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "mllib machine learning in apache spark"
    - "towards a unified architecture for in rdbms analytics"

Connected through hyperedge HE-4 (induced by source paper "mllib machine learning in apache spark"):
  with members:
    - "api design for machine learning software experiences from the scikit learn project"
    - "mli an api for distributed machine learning"

Connected through hyperedge HE-5 (induced by source paper "non stochastic best arm identification and hyperparameter optimization"):
  with members:
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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)