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: "batchbald efficient and diverse batch acquisition for deep bayesian active learning"
  Abstract: We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points jointly for the task of deep Bayesian active learning. BatchBALD is a greedy linear-time $1 - \frac{1}{e}$-approximate algorithm amenable to dynamic programming and efficient caching. We compare BatchBALD to the commonly used approach for batch data acquisition and find that the current approach acquires similar and redundant points, sometimes performing worse than randomly acquiring data. We finish by showing that, using BatchBALD to consider dependencies within an acquisition batch, we achieve new state of the art performance on standard benchmarks, providing substantial data efficiency improvements in batch acquisition.

Connected through hyperedge HE-1 (induced by source paper "task aware variational adversarial active learning"):
  with members:
    - "bayesian generative active deep learning"
    - "variational adversarial active learning"
    - "learning loss for active learning"
    - "ranking cgans subjective control over semantic image attributes"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "deep bayesian active learning with image data"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "information condensing active learning"):
  with members:
    - "understanding the limitations of variational mutual information estimators"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "adversarial active learning for deep networks a margin based approach"
    - "discriminative active learning"
    - "emnist an extension of mnist to handwritten letters"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "selection via proxy efficient data selection for deep learning"):
  with members:
    - "weight agnostic neural networks"
    - "how transferable are the datasets collected by active learners"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "on the relationship between data efficiency and error for uncertainty sampling"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "active learning for interactive neural machine translation of data streams"
    - "an empirical study of example forgetting during deep neural network learning"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-4 (induced by source paper "a new framework for query efficient active imitation learning"):
  with members:
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "one shot imitation learning"
    - "deep exploration via bootstrapped dqn"
    - "generative adversarial imitation learning"
    - "successor features for transfer in reinforcement learning"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

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)