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: "active one shot learning"
  Abstract: Recent advances in one-shot learning have produced models that can learn from a handful of labeled examples, for passive classification and regression tasks. This paper combines reinforcement learning with one-shot learning, allowing the model to decide, during classification, which examples are worth labeling. We introduce a classification task in which a stream of images are presented and, on each time step, a decision must be made to either predict a label or pay to receive the correct label. We present a recurrent neural network based action-value function, and demonstrate its ability to learn how and when to request labels. Through the choice of reward function, the model can achieve a higher prediction accuracy than a similar model on a purely supervised task, or trade prediction accuracy for fewer label requests.

Connected through hyperedge HE-1 (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-2 (induced by source paper "a survey on active learning and human in the loop deep learning for medical image analysis"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "continual lifelong learning with neural networks a review"
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "geometry in active learning for binary and multi class image segmentation"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"

Connected through hyperedge HE-3 (induced by source paper "picking groups instead of samples a close look at static pool based meta active learning"):
  with members:
    - "bayesian model agnostic meta learning"
    - "deep visual domain adaptation a survey"
    - "probabilistic model agnostic meta learning"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "learning algorithms for active learning"
    - "a simple neural attentive meta learner"

Connected through hyperedge HE-4 (induced by source paper "multi modal active learning from human data a deep reinforcement learning approach"):
  with members:
    - "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"
    - "personalized machine learning for robot perception of affect and engagement in autism therapy"
    - "multimodal machine translation with reinforcement learning"
    - "multimodal machine learning a survey and taxonomy"
    - "cost effective active learning for deep image classification"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "analyzing data selection techniques with tools from the theory of information losses"):
  with members:
    - "asymptotic finite sample information losses in neural classifiers"
    - "information losses in neural classifiers from sampling"
    - "interpreting active learning methods through information losses"
    - "approximation beats concentration an approximation view on inference with smooth radial kernels"
    - "mine mutual information neural estimation"
    - "learning how to active learn a deep reinforcement learning approach"
    - "opening the black box of deep neural networks via information"
    - "on the emergence of invariance and disentangling in deep representations"

Connected through hyperedge HE-6 (induced by source paper "the omniglot challenge a 3 year progress report"):
  with members:
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "prototypical networks for few shot learning"
    - "attentive recurrent comparators"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "matching networks for one shot learning"
    - "building machines that learn and think like people"

Connected through hyperedge HE-7 (induced by source paper "augmented memory networks for streaming based active one shot learning"):
  with members:
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "deep active learning for named entity recognition"
    - "cost effective active learning for deep image classification"
    - "learning how to active learn a deep reinforcement learning approach"
    - "ask the right questions active question reformulation with reinforcement learning"
    - "learning algorithms for active learning"
    - "deep active learning for dialogue generation"
    - "matching networks for one shot learning"

Connected through hyperedge HE-8 (induced by source paper "fast and effective adaptation of facial action unit detection deep model"):
  with members:
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "multi label zero shot learning with structured knowledge graphs"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "model agnostic meta learning for fast adaptation of deep networks"

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)