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: "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
  Abstract: The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity. Importantly, these changes are not passive, but are actively controlled by neuromodulation, which is itself under the control of the brain. The resulting self-modifying abilities of the brain play an important role in learning and adaptation, and are a major basis for biological reinforcement learning. Here we show for the first time that artificial neural networks with such neuromodulated plasticity can be trained with gradient descent. Extending previous work on differentiable Hebbian plasticity, we propose a differentiable formulation for the neuromodulation of plasticity. We show that neuromodulated plasticity improves the performance of neural networks on both reinforcement learning and supervised learning tasks. In one task, neuromodulated plastic LSTMs with millions of parameters outperform standard LSTMs on a benchmark language modeling task (controlling for the number of parameters). We conclude that differentiable neuromodulation of plasticity offers a powerful new framework for training neural networks.

Connected through hyperedge HE-1 (induced by source paper "finding online neural update rules by learning to remember"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "large scale adversarial representation learning"
    - "transformer xl attentive language models beyond a fixed length context"
    - "metalearned neural memory"
    - "does learning require memorization a short tale about a long tail"
    - "network of evolvable neural units evolving to learn at a synaptic level"
    - "shaping belief states with generative environment models for rl"

Connected through hyperedge HE-2 (induced by source paper "learning to continually learn"):
  with members:
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "meta learning representations for continual learning"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "continuous learning in single incremental task scenarios"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "explicit inductive bias for transfer learning with convolutional networks"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"

Connected through hyperedge HE-3 (induced by source paper "badger learning to learn learning algorithms through multi agent communication"):
  with members:
    - "efficient optimization of loops and limits with randomized telescoping sums"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "meta learning of sequential strategies"
    - "modularity as a means for complexity management in neural networks learning"
    - "a mathematical theory of cooperative communication"
    - "learning to communicate in multi agent reinforcement learning a review"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "weight agnostic neural networks"

Connected through hyperedge HE-4 (induced by source paper "meta learning with warped gradient descent"):
  with members:
    - "guided meta policy search"
    - "meta learning with differentiable convex optimization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta learning representations for continual learning"
    - "on first order meta learning algorithms"
    - "understanding short horizon bias in stochastic meta optimization"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-5 (induced by source paper "learning to remember from a multi task teacher"):
  with members:
    - "overcoming multi model forgetting"
    - "meta learning representations for continual learning"
    - "understanding and correcting pathologies in the training of learned optimizers"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "reviving and improving recurrent back propagation"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-6 (induced by source paper "meta learnt priors slow down catastrophic forgetting in neural networks"):
  with members:
    - "online meta learning"
    - "meta learning representations for continual learning"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "on first order meta learning algorithms"
    - "meta continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "meta learning by the baldwin effect"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"

Connected through hyperedge HE-7 (induced by source paper "introducing neuromodulation in deep neural networks to learn adaptive behaviours"):
  with members:
    - "conditional neural processes"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "proximal policy optimization algorithms"
    - "learning to reinforcement learn"
    - "asynchronous methods for deep reinforcement learning"
    - "benchmarking for bayesian reinforcement learning"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

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.NE(Neural and Evolutionary Computing)