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: "three scenarios for continual learning"
  Abstract: Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning difficult for machine learning. In recent years, numerous methods have been proposed for continual learning, but due to differences in evaluation protocols it is difficult to directly compare their performance. To enable more structured comparisons, we describe three continual learning scenarios based on whether at test time task identity is provided and--in case it is not--whether it must be inferred. Any sequence of well-defined tasks can be performed according to each scenario. Using the split and permuted MNIST task protocols, for each scenario we carry out an extensive comparison of recently proposed continual learning methods. We demonstrate substantial differences between the three scenarios in terms of difficulty and in terms of how efficient different methods are. In particular, when task identity must be inferred (i.e., class incremental learning), we find that regularization-based approaches (e.g., elastic weight consolidation) fail and that replaying representations of previous experiences seems required for solving this scenario.

Connected through hyperedge HE-1 (induced by source paper "triple memory networks a brain inspired method for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "end to end incremental learning"
    - "continual learning in generative adversarial nets"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"

Connected through hyperedge HE-2 (induced by source paper "targeted forgetting and false memory formation in continual learners through adversarial backdoor attacks"):
  with members:
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "task free continual learning"
    - "efficient lifelong learning with a gem"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "continual learning through synaptic intelligence"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"

Connected through hyperedge HE-3 (induced by source paper "unsupervised progressive learning and the stam architecture"):
  with members:
    - "lifelong machine learning with deep streaming linear discriminant analysis"
    - "continual lifelong learning with neural networks a review"
    - "continual learning via neural pruning"
    - "continual learning with tiny episodic memories"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-4 (induced by source paper "continual learning with hypernetworks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "large scale adversarial representation learning"
    - "high fidelity image generation with fewer labels"
    - "experience replay for continual learning"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "measuring and regularizing networks in function space"

Connected through hyperedge HE-5 (induced by source paper "continual unsupervised representation learning"):
  with members:
    - "unsupervised continual learning and self taught associative memory hierarchies"
    - "continual lifelong learning with neural networks a review"
    - "continual learning via neural pruning"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "efficient lifelong learning with a gem"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "dirichlet variational autoencoder"

Connected through hyperedge HE-6 (induced by source paper "adaptive masked proxies for few shot segmentation"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "no fuss distance metric learning using proxies"
    - "transfer of view manifold learning to similarity perception of novel objects"
    - "one shot learning for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "maintaining discrimination and fairness in class incremental learning"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "m2kd multi model and multi level knowledge distillation for incremental learning"
    - "label mapping neural networks with response consolidation for class incremental learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "continual learning with deep generative replay"
    - "one shot face recognition by promoting underrepresented classes"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-8 (induced by source paper "generative replay with feedback connections as a general strategy for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "incremental classifier learning with generative adversarial networks"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "deep generative dual memory network for continual learning"
    - "pseudo recursal solving the catastrophic forgetting problem in deep neural networks"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "a strategy for an uncompromising incremental learner"
    - "scalable recollections for continual lifelong learning"

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)