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: "batch active learning via coordinated matching"
  Abstract: Most prior work on active learning of classifiers has focused on sequentially selecting one unlabeled example at a time to be labeled in order to reduce the overall labeling effort. In many scenarios, however, it is desirable to label an entire batch of examples at once, for example, when labels can be acquired in parallel. This motivates us to study batch active learning, which iteratively selects batches of $k>1$ examples to be labeled. We propose a novel batch active learning method that leverages the availability of high-quality and efficient sequential active-learning policies by attempting to approximate their behavior when applied for $k$ steps. Specifically, our algorithm first uses Monte-Carlo simulation to estimate the distribution of unlabeled examples selected by a sequential policy over $k$ step executions. The algorithm then attempts to select a set of $k$ examples that best matches this distribution, leading to a combinatorial optimization problem that we term "bounded coordinated matching". While we show this problem is NP-hard in general, we give an efficient greedy solution, which inherits approximation bounds from supermodular minimization theory. Our experimental results on eight benchmark datasets show that the proposed approach is highly effective

Connected through hyperedge HE-1 (induced by source paper "batch active learning using determinantal point processes"):
  with members:
    - "diverse mini batch active learning"
    - "uncertainty sampling is preconditioned stochastic gradient descent on zero one loss"
    - "active model learning and diverse action sampling for task and motion planning"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "batch active preference based learning of reward functions"
    - "determinantal point processes for mini batch diversification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning preferences for manipulation tasks from online coactive feedback"

Connected through hyperedge HE-2 (induced by source paper "batchbald efficient and diverse batch acquisition for deep bayesian active learning"):
  with members:
    - "variational adversarial active learning"
    - "cinic 10 is not imagenet or cifar 10"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "deep active learning for named entity recognition"
    - "cost effective active learning for deep image classification"
    - "deep bayesian active learning with image data"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"

Connected through hyperedge HE-3 (induced by source paper "discriminative active learning"):
  with members:
    - "adversarial active learning for deep networks a margin based approach"
    - "generative adversarial active learning"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "active learning for speech recognition the power of gradients"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "parallelizing exploration exploitation tradeoffs with gaussian process bandit optimization"):
  with members:
    - "online learning under delayed feedback"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "near optimal nonmyopic value of information in graphical models"
    - "hybrid batch bayesian optimization"
    - "efficient optimal learning for contextual bandits"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"
    - "multi armed bandits in metric spaces"

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)