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: "the omniglot challenge a 3 year progress report"
  Abstract: Three years ago, we released the Omniglot dataset for one-shot learning, along with five challenge tasks and a computational model that addresses these tasks. The model was not meant to be the final word on Omniglot; we hoped that the community would build on our work and develop new approaches. In the time since, we have been pleased to see wide adoption of the dataset. There has been notable progress on one-shot classification, but researchers have adopted new splits and procedures that make the task easier. There has been less progress on the other four tasks. We conclude that recent approaches are still far from human-like concept learning on Omniglot, a challenge that requires performing many tasks with a single model.

Connected through hyperedge HE-1 (induced by source paper "compositional generalization through meta sequence to sequence learning"):
  with members:
    - "learning to infer program sketches"
    - "human few shot learning of compositional instructions"
    - "good enough compositional data augmentation"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "mutual exclusivity as a challenge for deep neural networks"
    - "systematic generalization what is required and can it be learned"
    - "jump to better conclusions scan both left and right"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-2 (induced by source paper "torchmeta a meta learning library for pytorch"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "fast context adaptation via meta learning"
    - "meta learning with differentiable closed form solvers"
    - "probabilistic model agnostic meta learning"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-3 (induced by source paper "aha an artificial hippocampal algorithm for episodic machine learning"):
  with members:
    - "how can we be so dense the benefits of using highly sparse representations"
    - "boosting few shot visual learning with self supervision"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "unsupervised predictive memory in a goal directed agent"
    - "dynamic routing between capsules"
    - "adam a method for stochastic optimization"
    - "winner take all autoencoders"
    - "k sparse autoencoders"

Connected through hyperedge HE-4 (induced by source paper "people infer recursive visual concepts from just a few examples"):
  with members:
    - "compositional generalization through meta sequence to sequence learning"
    - "formalizing neurath s ship approximate algorithms for online causal learning"
    - "question asking as program generation"
    - "learning to infer graphics programs from hand drawn images"
    - "building machines that learn and think like people"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "the tcga meta dataset clinical benchmark"):
  with members:
    - "torchmeta a meta learning library for pytorch"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "deep filter banks for texture recognition description and segmentation"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"
    - "microsoft coco common objects in context"
    - "fine grained visual classification of aircraft"

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)