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: "diffusion based neuromodulation can eliminate catastrophic forgetting in simple neural networks"
  Abstract: A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules). While the prior work did show that modular ANNs suffered less from catastrophic forgetting, it was not able to produce ANNs that possessed task-specific functional modules, thereby leaving the main theory regarding modularity and forgetting untested. We introduce diffusion-based neuromodulation, which simulates the release of diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up or down regulate) learning in a spatial region. On the simple diagnostic problem from the prior work, diffusion-based neuromodulation 1) induces task-specific learning in groups of nodes and connections (task-specific localized learning), which 2) produces functional modules for each subtask, and 3) yields higher performance by eliminating catastrophic forgetting. Overall, our results suggest that diffusion-based neuromodulation promotes task-specific localized learning and functional modularity, which can help solve the challenging, but important problem of catastrophic forgetting.

Connected through hyperedge HE-1 (induced by source paper "learning to continually learn"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"):
  with members:
    - "meta learning autoencoders for few shot prediction"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "regularizing and optimizing lstm language models"
    - "learning to reinforcement learn"
    - "end to end memory networks"
    - "recurrent neural network regularization"

Connected through hyperedge HE-3 (induced by source paper "guiding neuroevolution with structural objectives"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "the evolutionary origins of hierarchy"
    - "very deep convolutional networks for large scale image recognition"
    - "evolution of a subsumption architecture neurocontroller"

Connected through hyperedge HE-4 (induced by source paper "go explore a new approach for hard exploration problems"):
  with members:
    - "fractal ai a fragile theory of intelligence"
    - "exploration by random network distillation"
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-5 (induced by source paper "born to learn the inspiration progress and future of evolved plastic artificial neural networks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "evolving deep neural networks"
    - "lstm a search space odyssey"
    - "progressive neural networks"
    - "hierarchical representations for efficient architecture search"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "large scale evolution of convolutional neural networks using volunteer computing"

Connected through hyperedge HE-6 (induced by source paper "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"):
  with members:
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "overcoming catastrophic interference by conceptors"
    - "continual learning through synaptic intelligence"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "one shot learning with memory augmented neural networks"

Connected through hyperedge HE-7 (induced by source paper "combating catastrophic forgetting with developmental compression"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "progressive neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "a minimal developmental model can increase evolvability in soft robots"
    - "safe mutations for deep and recurrent neural networks through output gradients"
    - "dynamic routing between capsules"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "self adaptation of mutation rates in non elitist populations"

Connected through hyperedge HE-8 (induced by source paper "interoceptive robustness through environment mediated morphological development"):
  with members:
    - "evolving deep neural networks"
    - "a minimal developmental model can increase evolvability in soft robots"
    - "synthesizing robust adversarial examples"
    - "neural architecture search with reinforcement learning"
    - "robots that can adapt like animals"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "intriguing properties of neural networks"

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)