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: "learning to multi task by active sampling"
  Abstract: One of the long-standing challenges in Artificial Intelligence for learning goal-directed behavior is to build a single agent which can solve multiple tasks. Recent progress in multi-task learning for goal-directed sequential problems has been in the form of distillation based learning wherein a student network learns from multiple task-specific expert networks by mimicking the task-specific policies of the expert networks. While such approaches offer a promising solution to the multi-task learning problem, they require supervision from large expert networks which require extensive data and computation time for training. In this work, we propose an efficient multi-task learning framework which solves multiple goal-directed tasks in an on-line setup without the need for expert supervision. Our work uses active learning principles to achieve multi-task learning by sampling the harder tasks more than the easier ones. We propose three distinct models under our active sampling framework. An adaptive method with extremely competitive multi-tasking performance. A UCB-based meta-learner which casts the problem of picking the next task to train on as a multi-armed bandit problem. A meta-learning method that casts the next-task picking problem as a full Reinforcement Learning problem and uses actor critic methods for optimizing the multi-tasking performance directly. We demonstrate results in the Atari 2600 domain on seven multi-tasking instances: three 6-task instances, one 8-task instance, two 12-task instances and one 21-task instance.

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"
    - "meta learning transferable active learning policies by deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "controllable and progressive image extrapolation"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "foreground aware image inpainting"
    - "semantic image synthesis with spatially adaptive normalization"
    - "very long natural scenery image prediction by outpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "non stationary texture synthesis by adversarial expansion"
    - "linknet relational embedding for scene graph"

Connected through hyperedge HE-3 (induced by source paper "feature partitioning for efficient multi task architectures"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "multi task zipping via layer wise neuron sharing"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "combating catastrophic forgetting with developmental compression"
    - "unifying and merging well trained deep neural networks for inference stage"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "evolutionary architecture search for deep multitask networks"

Connected through hyperedge HE-4 (induced by source paper "learning abstract options"):
  with members:
    - "automated curriculum learning for neural networks"
    - "learning to compose skills"
    - "progressive neural networks"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "learnings options end to end for continuous action tasks"
    - "representation stability as a regularizer for improved text analytics transfer learning"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-5 (induced by source paper "region growing curriculum generation for reinforcement learning"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "automatic goal generation for reinforcement learning agents"
    - "teacher student curriculum learning"
    - "reverse curriculum generation for reinforcement learning"
    - "intrinsic motivation and automatic curricula via asymmetric self play"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "count based exploration with neural density models"
    - "count based exploration in feature space for reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "an active learning framework for efficient robust policy search"):
  with members:
    - "model ensemble trust region policy optimization"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "proximal policy optimization algorithms"
    - "sample efficient actor critic with experience replay"
    - "epopt learning robust neural network policies using model ensembles"
    - "high dimensional continuous control using generalized advantage estimation"
    - "actor mimic deep multitask and transfer reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "training medical image analysis systems like radiologists"):
  with members:
    - "teacher student curriculum learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "a multi armed bandit to smartly select a training set from big medical data"
    - "revealing hidden potentials of the q space signal in breast cancer"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "teacher student curriculum learning"):
  with members:
    - "automatic goal generation for reinforcement learning agents"
    - "faster reinforcement learning using active simulators"
    - "intrinsic motivation and automatic curricula via asymmetric self play"
    - "proximal policy optimization algorithms"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "reinforcement learning through asynchronous advantage actor critic on a gpu"
    - "vime variational information maximizing exploration"
    - "control of memory active perception and action in minecraft"

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.NE(Neural and Evolutionary Computing)