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: "task free continual learning"
  Abstract: Methods proposed in the literature towards continual deep learning typically operate in a task-based sequential learning setup. A sequence of tasks is learned, one at a time, with all data of current task available but not of previous or future tasks. Task boundaries and identities are known at all times. This setup, however, is rarely encountered in practical applications. Therefore we investigate how to transform continual learning to an online setup. We develop a system that keeps on learning over time in a streaming fashion, with data distributions gradually changing and without the notion of separate tasks. To this end, we build on the work on Memory Aware Synapses, and show how this method can be made online by providing a protocol to decide i) when to update the importance weights, ii) which data to use to update them, and iii) how to accumulate the importance weights at each update step. Experimental results show the validity of the approach in the context of two applications: (self-)supervised learning of a face recognition model by watching soap series and learning a robot to avoid collisions.

Connected through hyperedge HE-1 (induced by source paper "on catastrophic interference in atari 2600 games"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "continual unsupervised representation learning"
    - "continual lifelong learning with neural networks a review"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "benchmarking bonus based exploration methods on the arcade learning environment"
    - "dopamine a research framework for deep reinforcement learning"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-2 (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"
    - "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"
    - "efficient lifelong learning with a gem"

Connected through hyperedge HE-3 (induced by source paper "targeted forgetting and false memory formation in continual learners through adversarial backdoor attacks"):
  with members:
    - "three scenarios for continual learning"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "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-4 (induced by source paper "continual learning in neural networks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "knowledge transfer in deep block modular neural networks"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "experience replay for continual learning"

Connected through hyperedge HE-5 (induced by source paper "continual learning a comparative study on how to defy forgetting in classification tasks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "continual learning for robotics"
    - "continual lifelong learning with neural networks a review"
    - "a comprehensive application oriented study of catastrophic forgetting in dnns"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "experience replay for continual learning"

Connected through hyperedge HE-6 (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"
    - "three scenarios for continual learning"
    - "continual learning with tiny episodic memories"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-7 (induced by source paper "using hindsight to anchor past knowledge in continual learning"):
  with members:
    - "dataset distillation"
    - "continual learning with tiny episodic memories"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "modular meta learning"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"

Connected through hyperedge HE-8 (induced by source paper "continuous adaptation for interactive object segmentation by learning from corrections"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "large scale interactive object segmentation with human annotators"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "interactive video object segmentation in the wild"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "iteratively trained interactive segmentation"

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.CV(Computer Vision and Pattern Recognition)