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 big data bootstrap"
  Abstract: The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets, the computation of bootstrap-based quantities can be prohibitively demanding. As an alternative, we present the Bag of Little Bootstraps (BLB), a new procedure which incorporates features of both the bootstrap and subsampling to obtain a robust, computationally efficient means of assessing estimator quality. BLB is well suited to modern parallel and distributed computing architectures and retains the generic applicability, statistical efficiency, and favorable theoretical properties of the bootstrap. We provide the results of an extensive empirical and theoretical investigation of BLB's behavior, including a study of its statistical correctness, its large-scale implementation and performance, selection of hyperparameters, and performance on real data.

Connected through hyperedge HE-1 (induced by source paper "residual reactive navigation combining classical and learned navigation strategies for deployment in unknown environments"):
  with members:
    - "estimating risk and uncertainty in deep reinforcement learning"
    - "policies modulating trajectory generators"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "residual policy learning"
    - "on evaluation of embodied navigation agents"
    - "residual reinforcement learning for robot control"
    - "addressing function approximation error in actor critic methods"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "verdictdb universalizing approximate query processing"):
  with members:
    - "database learning toward a database that becomes smarter every time"
    - "visualization aware sampling for very large databases"
    - "approximate lifted inference with probabilistic databases"
    - "a sampling algebra for aggregate estimation"
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "early accurate results for advanced analytics on mapreduce"

Connected through hyperedge HE-3 (induced by source paper "sampling without compromising accuracy in adaptive data analysis"):
  with members:
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "max information differential privacy and post selection hypothesis testing"
    - "differentially private release and learning of threshold functions"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "generalization in adaptive data analysis and holdout reuse"
    - "between pure and approximate differential privacy"
    - "algorithmic stability for adaptive data analysis"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-4 (induced by source paper "drop dimensionality reduction optimization for time series"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "macrobase prioritizing attention in fast data"
    - "fma a dataset for music analysis"
    - "effortless data exploration with zenvisage an expressive and interactive visual analytics system"
    - "training region based object detectors with online hard example mining"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "spectrum estimation from samples"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"

Connected through hyperedge HE-5 (induced by source paper "a pliable index coding approach to data shuffling"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "near optimal coded data shuffling for distributed learning"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "a unified coding framework for distributed computing with straggling servers"
    - "information theoretic limits of data shuffling for distributed learning"
    - "the minrank of random graphs"
    - "a polynomial time algorithm for pliable index coding"
    - "content type coding"

Connected through hyperedge HE-6 (induced by source paper "uncertainty aware reinforcement learning for collision avoidance"):
  with members:
    - "deep exploration via bootstrapped dqn"
    - "funnel libraries for real time robust feedback motion planning"
    - "introspective perception learning to predict failures in vision systems"
    - "plato policy learning using adaptive trajectory optimization"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-7 (induced by source paper "deep metric learning via facility location"):
  with members:
    - "improving the robustness of deep neural networks via stability training"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"
    - "training deep networks with structured layers by matrix backpropagation"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "deep metric learning via lifted structured feature embedding"

Connected through hyperedge HE-8 (induced by source paper "big graph search challenges and techniques"):
  with members:
    - "distributed graphlab a framework for machine learning in the cloud"
    - "capturing topology in graph pattern matching"

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.LG(Machine Learning)