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: "meta learning transferable active learning policies by deep reinforcement learning"
  Abstract: Active learning (AL) aims to enable training high performance classifiers with low annotation cost by predicting which subset of unlabelled instances would be most beneficial to label. The importance of AL has motivated extensive research, proposing a wide variety of manually designed AL algorithms with diverse theoretical and intuitive motivations. In contrast to this body of research, we propose to treat active learning algorithm design as a meta-learning problem and learn the best criterion from data. We model an active learning algorithm as a deep neural network that inputs the base learner state and the unlabelled point set and predicts the best point to annotate next. Training this active query policy network with reinforcement learning, produces the best non-myopic policy for a given dataset. The key challenge in achieving a general solution to AL then becomes that of learner generalisation, particularly across heterogeneous datasets. We propose a multi-task dataset-embedding approach that allows dataset-agnostic active learners to be trained. Our evaluation shows that AL algorithms trained in this way can directly generalize across diverse problems.

Connected through hyperedge HE-1 (induced by source paper "revisiting meta learning as supervised learning"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "task2vec task embedding for meta learning"
    - "a closer look at few shot classification"
    - "the information complexity of learning tasks their structure and their distance"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning a survey"
    - "meta learning with latent embedding optimization"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"

Connected through hyperedge HE-2 (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"
    - "discovering general purpose active learning strategies"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "dsnet for real time driving scene semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "dynamic face video segmentation via reinforcement learning"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "face mask extraction in video sequence"
    - "architecture search of dynamic cells for semantic video segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "inter bmv interpolation with block motion vectors for fast semantic segmentation on video"

Connected through hyperedge HE-4 (induced by source paper "bias aware heapified policy for active learning"):
  with members:
    - "dataset distillation"
    - "active adversarial domain adaptation"
    - "adversarial active learning for deep networks a margin based approach"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "leveraging motion priors in videos for improving human segmentation"
    - "discriminative active learning"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"

Connected through hyperedge HE-5 (induced by source paper "augmented memory networks for streaming based active one shot learning"):
  with members:
    - "active one shot 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-6 (induced by source paper "large scale visual active learning with deep probabilistic ensembles"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"
    - "model based active exploration"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "on calibration of modern neural networks"
    - "active one shot learning"
    - "learning how to active learn a deep reinforcement learning approach"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-7 (induced by source paper "discovering general purpose active learning strategies"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "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"
    - "a meta learning approach to one step active learning"
    - "embodied question answering"
    - "learning to look around intelligently exploring unseen environments for unknown tasks"

Connected through hyperedge HE-8 (induced by source paper "learning to teach in cooperative multiagent reinforcement learning"):
  with members:
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "automated curriculum learning for neural networks"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning with opponent learning awareness"
    - "emergence of grounded compositional language in multi agent populations"
    - "coordinated multi agent imitation learning"
    - "learning algorithms for active learning"
    - "learning to teach reinforcement learning agents"

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)