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: "efficient lifelong learning with a gem"
  Abstract: In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task. In this work, we investigate the efficiency of current lifelong approaches, in terms of sample complexity, computational and memory cost. Towards this end, we first introduce a new and a more realistic evaluation protocol, whereby learners observe each example only once and hyper-parameter selection is done on a small and disjoint set of tasks, which is not used for the actual learning experience and evaluation. Second, we introduce a new metric measuring how quickly a learner acquires a new skill. Third, we propose an improved version of GEM (Lopez-Paz & Ranzato, 2017), dubbed Averaged GEM (A-GEM), which enjoys the same or even better performance as GEM, while being almost as computationally and memory efficient as EWC (Kirkpatrick et al., 2016) and other regularization-based methods. Finally, we show that all algorithms including A-GEM can learn even more quickly if they are provided with task descriptors specifying the classification tasks under consideration. Our experiments on several standard lifelong learning benchmarks demonstrate that A-GEM has the best trade-off between accuracy and efficiency.

Connected through hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning"):
  with members:
    - "learning to continually learn"
    - "fine grained continual learning"
    - "continuous meta learning without tasks"
    - "reinforcement learning in non stationary environments"
    - "online learned continual compression with stacked quantization modules"
    - "continual lifelong learning with neural networks a review"
    - "online meta learning"
    - "regularization shortcomings for continual learning"

Connected through hyperedge HE-2 (induced by source paper "incremental object detection via meta learning"):
  with members:
    - "meta curvature"
    - "rilod near real time incremental learning for object detection at the edge"
    - "meta r cnn towards general solver for instance level few shot learning"
    - "meta learning without memorization"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "few shot object detection via feature reweighting"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-3 (induced by source paper "gradient surgery for multi task learning"):
  with members:
    - "distilling policy distillation"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "branched multi task networks deciding what layers to share"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "routing networks and the challenges of modular and compositional computation"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "attentive single tasking of multiple tasks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-4 (induced by source paper "a neural dirichlet process mixture model for task free continual learning"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "on tiny episodic memories in continual learning"
    - "continual lifelong learning with neural networks a review"
    - "gradient based sample selection for online continual learning"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "reconciling meta learning and continual learning with online mixtures of tasks"
    - "continuous learning in single incremental task scenarios"

Connected through hyperedge HE-5 (induced by source paper "ternary feature masks continual learning without any forgetting"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-6 (induced by source paper "batchensemble an alternative approach to efficient ensemble and lifelong learning"):
  with members:
    - "analyzing the role of model uncertainty for electronic health records"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "experience replay for continual learning"
    - "reinforced continual learning"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "on calibration of modern neural networks"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "incremental learning for metric based meta learners"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "online meta learning"
    - "incremental few shot learning for pedestrian attribute recognition"
    - "few shot learning with embedded class models and shot free meta training"
    - "meta learning with latent embedding optimization"
    - "incremental few shot learning with attention attractor networks"
    - "dynamic few shot visual learning without forgetting"
    - "the devil is in the tails fine grained classification in the wild"

Connected through hyperedge HE-8 (induced by source paper "federated continual learning with adaptive parameter communication"):
  with members:
    - "federated learning with matched averaging"
    - "scalable and order robust continual learning with additive parameter decomposition"
    - "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"
    - "asynchronous online federated learning for edge devices"
    - "federated optimization for heterogeneous networks"
    - "reinforced continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "progressive neural 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)