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: "a meta learning approach to one step active learning"
  Abstract: We consider the problem of learning when obtaining the training labels is costly, which is usually tackled in the literature using active-learning techniques. These approaches provide strategies to choose the examples to label before or during training. These strategies are usually based on heuristics or even theoretical measures, but are not learned as they are directly used during training. We design a model which aims at \textit{learning active-learning strategies} using a meta-learning setting. More specifically, we consider a pool-based setting, where the system observes all the examples of the dataset of a problem and has to choose the subset of examples to label in a single shot. Experiments show encouraging results.

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 "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"
    - "active one shot learning"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "learning algorithms for active learning"

Connected through hyperedge HE-3 (induced by source paper "active learning for graph neural networks via node feature propagation"):
  with members:
    - "simplifying graph convolutional networks"
    - "gradient descent finds global minima of deep neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "junction tree variational autoencoder for molecular graph generation"
    - "deep active learning for named entity recognition"
    - "active learning for graph embedding"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "visual curiosity learning to ask questions to learn visual recognition"):
  with members:
    - "neural baby talk"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning algorithms for active learning"
    - "recognizing objects in the wild where do we stand"
    - "learning how to learn an adaptive dialogue agent for incrementally learning visually grounded word meanings"
    - "iqa visual question answering in interactive environments"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "embodied question answering"

Connected through hyperedge HE-5 (induced by source paper "discovering general purpose active learning strategies"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "active one shot learning"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning to reason end to end module networks for visual question answering"
    - "learning algorithms for active learning"
    - "embodied question answering"
    - "learning to look around intelligently exploring unseen environments for unknown tasks"

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)