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: "continual reinforcement learning with complex synapses"
  Abstract: Unlike humans, who are capable of continual learning over their lifetimes, artificial neural networks have long been known to suffer from a phenomenon known as catastrophic forgetting, whereby new learning can lead to abrupt erasure of previously acquired knowledge. Whereas in a neural network the parameters are typically modelled as scalar values, an individual synapse in the brain comprises a complex network of interacting biochemical components that evolve at different timescales. In this paper, we show that by equipping tabular and deep reinforcement learning agents with a synaptic model that incorporates this biological complexity (Benna & Fusi, 2016), catastrophic forgetting can be mitigated at multiple timescales. In particular, we find that as well as enabling continual learning across sequential training of two simple tasks, it can also be used to overcome within-task forgetting by reducing the need for an experience replay database.

Connected through hyperedge HE-1 (induced by source paper "orthogonal gradient descent for continual learning"):
  with members:
    - "prototype reminding for continual learning"
    - "uncertainty guided continual learning with bayesian neural networks"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "towards federated learning at scale system design"
    - "variational prototype replays for continual learning"
    - "attention based structural plasticity"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"

Connected through hyperedge HE-2 (induced by source paper "merging deterministic policy gradient estimations with varied bias variance tradeoff for effective deep reinforcement learning"):
  with members:
    - "regularized anderson acceleration for off policy deep reinforcement learning"
    - "policy consolidation for continual reinforcement learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "a theory of regularized markov decision processes"
    - "importance resampling for off policy prediction"
    - "lookahead optimizer k steps forward 1 step back"
    - "understanding short horizon bias in stochastic meta optimization"
    - "the mirage of action dependent baselines in reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "meta learning of sequential strategies"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "causal reasoning from meta reinforcement learning"
    - "conditional neural processes"
    - "sample efficient adaptive text to speech"
    - "modeling friends and foes"

Connected through hyperedge HE-4 (induced by source paper "continual reinforcement learning with diversity exploration and adversarial self correction"):
  with members:
    - "marathon environments multi agent continuous control benchmarks in a modern video game engine"
    - "deepmind control suite"
    - "deep reinforcement learning for autonomous driving"
    - "an introduction to deep reinforcement learning"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "diversity is all you need learning skills without a reward function"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"

Connected through hyperedge HE-5 (induced by source paper "continual reinforcement learning in 3d non stationary environments"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "online meta learning"
    - "model primitive hierarchical lifelong reinforcement learning"
    - "task free continual learning"
    - "don t forget there is more than forgetting new metrics for continual learning"
    - "continuous learning in single incremental task scenarios"
    - "accelerated methods for deep reinforcement learning"
    - "state representation learning for control an overview"

Connected through hyperedge HE-6 (induced by source paper "grim repr prioritising generating important features for pseudo rehearsal"):
  with members:
    - "closed loop memory gan for continual learning"
    - "generative adversarial networks and perceptual losses for video super resolution"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting"
    - "deep generative dual memory network for continual learning"
    - "pseudo recursal solving the catastrophic forgetting problem in deep neural networks"
    - "progressive neural networks"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-7 (induced by source paper "reducing catastrophic forgetting in modular neural networks by dynamic information balancing"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "isbnet instance aware selective branching networks"
    - "selective experience replay for lifelong learning"
    - "continual learning through synaptic intelligence"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "multigrid neural architectures"
    - "overcoming catastrophic forgetting in neural networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-8 (induced by source paper "weight friction a simple method to overcome catastrophic forgetting and enable continual learning"):
  with members:
    - "efficient lifelong learning with a gem"
    - "progressive neural networks"
    - "gradient episodic memory for continual learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep learning in neural networks"
    - "deep domain confusion maximizing for domain invariance"

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.AI(Artificial Intelligence)