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: "deep bayesian active learning with image data"
  Abstract: Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) methods generally rely on being able to learn and update models from small amounts of data. Recent advances in deep learning, on the other hand, are notorious for their dependence on large amounts of data. Second, many AL acquisition functions rely on model uncertainty, yet deep learning methods rarely represent such model uncertainty. In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way. We develop an active learning framework for high dimensional data, a task which has been extremely challenging so far, with very sparse existing literature. Taking advantage of specialised models such as Bayesian convolutional neural networks, we demonstrate our active learning techniques with image data, obtaining a significant improvement on existing active learning approaches. We demonstrate this on both the MNIST dataset, as well as for skin cancer diagnosis from lesion images (ISIC2016 task).

Connected through hyperedge HE-1 (induced by source paper "action for better prediction"):
  with members:
    - "self supervised exploration via disagreement"
    - "curious ilqr resolving uncertainty in model based rl"
    - "model based reinforcement learning for atari"
    - "manipulation by feel touch based control with deep predictive models"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "robonet large scale multi robot learning"
    - "learning predictive models from observation and interaction"
    - "exploration by random network distillation"

Connected through hyperedge HE-2 (induced by source paper "active and incremental learning with weak supervision"):
  with members:
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "yolov3 an incremental improvement"
    - "active learning for deep object detection"
    - "focal loss for dense object detection"
    - "cost effective active learning for deep image classification"
    - "extreme clicking for efficient object annotation"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "automatically identifying wild animals in camera trap images with deep learning"

Connected through hyperedge HE-3 (induced by source paper "reinforced active learning for image segmentation"):
  with members:
    - "multi target embodied question answering"
    - "application of decision rules for handling class imbalance in semantic segmentation"
    - "panoptic feature pyramid networks"
    - "cereals cost effective region based active learning for semantic segmentation"
    - "learning a policy for opportunistic active learning"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "discovering general purpose active learning strategies"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"

Connected through hyperedge HE-4 (induced by source paper "scalable uncertainty for computer vision with functional variational inference"):
  with members:
    - "deepfactors real time probabilistic dense monocular slam"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "functional variational bayesian neural networks"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "evaluating bayesian deep learning methods for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "right for the wrong scientific reasons revising deep networks by interacting with their explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"
    - "this looks like that deep learning for interpretable image recognition"
    - "explaining explanations an overview of interpretability of machine learning"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"

Connected through hyperedge HE-6 (induced by source paper "task aware variational adversarial active learning"):
  with members:
    - "bayesian generative active deep learning"
    - "batchbald efficient and diverse batch acquisition for deep bayesian active 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"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-7 (induced by source paper "deep active learning for biased datasets via fisher kernel self supervision"):
  with members:
    - "remap multi layer entropy guided pooling of dense cnn features for image retrieval"
    - "variational adversarial active learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "interpreting black box predictions using fisher kernels"
    - "unsupervised representation learning by predicting image rotations"
    - "learning to reweight examples for robust deep learning"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

Connected through hyperedge HE-8 (induced by source paper "active learning for entity alignment"):
  with members:
    - "active learning for graph neural networks via node feature propagation"
    - "knowledge graph entity alignment with graph convolutional networks lessons learned"
    - "knowledge graph alignment network with gated multi hop neighborhood aggregation"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "lasagne locality and structure aware graph node embedding"
    - "active learning for graph embedding"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "deep active learning over the long tail"

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)