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: "metalearning with hebbian fast weights"
  Abstract: We unify recent neural approaches to one-shot learning with older ideas of associative memory in a model for metalearning. Our model learns jointly to represent data and to bind class labels to representations in a single shot. It builds representations via slow weights, learned across tasks through SGD, while fast weights constructed by a Hebbian learning rule implement one-shot binding for each new task. On the Omniglot, Mini-ImageNet, and Penn Treebank one-shot learning benchmarks, our model achieves state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "comparison network for one shot conditional object detection"):
  with members:
    - "meta learning with latent embedding optimization"
    - "zero shot object detection by hybrid region embedding"
    - "large margin few shot learning"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-2 (induced by source paper "metalearned neural memory"):
  with members:
    - "contextual memory trees"
    - "learning attractor dynamics for generative memory"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "learning to reason with third order tensor products"
    - "one shot visual imitation learning via meta learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "rapid adaptation with conditionally shifted neurons"
    - "a simple neural attentive meta learner"

Connected through hyperedge HE-3 (induced by source paper "online gradient based mixtures for transfer modulation in meta learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "probabilistic model agnostic meta learning"
    - "the effects of negative adaptation in model agnostic meta learning"
    - "dynamic few shot visual learning without forgetting"
    - "prototypical networks for few shot learning"
    - "continual learning through synaptic intelligence"

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)