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 learn without forgetting by maximizing transfer and minimizing interference"
  Abstract: Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples. We then propose a new algorithm, Meta-Experience Replay (MER), that directly exploits this view by combining experience replay with optimization based meta-learning. This method learns parameters that make interference based on future gradients less likely and transfer based on future gradients more likely. We conduct experiments across continual lifelong supervised learning benchmarks and non-stationary reinforcement learning environments demonstrating that our approach consistently outperforms recently proposed baselines for continual learning. Our experiments show that the gap between the performance of MER and baseline algorithms grows both as the environment gets more non-stationary and as the fraction of the total experiences stored gets smaller.

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 "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-3 (induced by source paper "multi source domain adaptation in the deep learning era a systematic survey"):
  with members:
    - "multi source domain adaptation for text classification via distancenet bandits"
    - "multi source domain adaptation for visual sentiment classification"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "multi source domain adaptation for semantic segmentation"
    - "multi source distilling domain adaptation"
    - "moment matching for multi source domain adaptation"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "interference and generalization in temporal difference learning"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards characterizing divergence in deep q learning"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "stiffness a new perspective on generalization in neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "on first order meta learning algorithms"
    - "natural environment benchmarks for reinforcement learning"

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"
    - "efficient lifelong learning with a gem"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "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"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "on calibration of modern neural networks"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "online meta critic learning for off policy actor critic methods"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "addressing the loss metric mismatch with adaptive loss alignment"
    - "learning surrogate losses"
    - "learning to teach with dynamic loss functions"
    - "meta gradient reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "meta reinforcement learning of structured exploration strategies"

Connected through hyperedge HE-8 (induced by source paper "mnemonics training multi class incremental learning without forgetting"):
  with members:
    - "large scale incremental learning"
    - "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
    - "pytorch an imperative style high performance deep learning library"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "meta transfer learning for few shot learning"
    - "deep generative dual memory network for continual learning"
    - "end to end incremental learning"
    - "a strategy for an uncompromising incremental learner"

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)