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: "neural program meta induction"
  Abstract: Most recently proposed methods for Neural Program Induction work under the assumption of having a large set of input/output (I/O) examples for learning any underlying input-output mapping. This paper aims to address the problem of data and computation efficiency of program induction by leveraging information from related tasks. Specifically, we propose two approaches for cross-task knowledge transfer to improve program induction in limited-data scenarios. In our first proposal, portfolio adaptation, a set of induction models is pretrained on a set of related tasks, and the best model is adapted towards the new task using transfer learning. In our second approach, meta program induction, a $k$-shot learning approach is used to make a model generalize to new tasks without additional training. To test the efficacy of our methods, we constructed a new benchmark of programs written in the Karel programming language. Using an extensive experimental evaluation on the Karel benchmark, we demonstrate that our proposals dramatically outperform the baseline induction method that does not use knowledge transfer. We also analyze the relative performance of the two approaches and study conditions in which they perform best. In particular, meta induction outperforms all existing approaches under extreme data sparsity (when a very small number of examples are available), i.e., fewer than ten. As the number of available I/O examples increase (i.e. a thousand or more), portfolio adapted program induction becomes the best approach. For intermediate data sizes, we demonstrate that the combined method of adapted meta program induction has the strongest performance.

Connected through hyperedge HE-1 (induced by source paper "improving molecular design by stochastic iterative target augmentation"):
  with members:
    - "infograph unsupervised and semi supervised graph level representation learning via mutual information maximization"
    - "conditional molecular design with deep generative models"
    - "pre training graph neural networks"
    - "unsupervised data augmentation for consistency training"
    - "mixmatch a holistic approach to semi supervised learning"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "deep reinforcement learning for de novo drug design"
    - "design by adaptive sampling"

Connected through hyperedge HE-2 (induced by source paper "high level plan for behavioral robot navigation with natural language directions and r net"):
  with members:
    - "a deep learning based behavioral approach to indoor autonomous navigation"
    - "translating navigation instructions in natural language to a high level plan for behavioral robot navigation"

Connected through hyperedge HE-3 (induced by source paper "learning compositional neural programs with recursive tree search and planning"):
  with members:
    - "learning to infer program sketches"
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "ranked reward enabling self play reinforcement learning for combinatorial optimization"
    - "hierarchical reinforcement learning with hindsight"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "improving the universality and learnability of neural programmer interpreters with combinator abstraction"
    - "fast context adaptation via meta learning"

Connected through hyperedge HE-4 (induced by source paper "program synthesis and semantic parsing with learned code idioms"):
  with members:
    - "encoding database schemas with relation aware self attention for text to sql parsers"
    - "learning programmatic idioms for scalable semantic parsing"
    - "a grammar based structural cnn decoder for code generation"
    - "self attention with relative position representations"
    - "generative code modeling with graphs"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "coarse to fine decoding for neural semantic parsing"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"

Connected through hyperedge HE-5 (induced by source paper "modularity as a means for complexity management in neural networks learning"):
  with members:
    - "neural program synthesis with priority queue training"
    - "explainable neural computation via stack neural module networks"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "attention is all you need"
    - "making neural programming architectures generalize via recursion"
    - "learning to reason end to end module networks for visual question answering"
    - "massive exploration of neural machine translation architectures"

Connected through hyperedge HE-6 (induced by source paper "natural language to structured query generation via meta learning"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "learning to optimize neural nets"
    - "learning a neural semantic parser from user feedback"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "matching networks for one shot learning"

Connected through hyperedge HE-7 (induced by source paper "leveraging grammar and reinforcement learning for neural program synthesis"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "a syntactic neural model for general purpose code generation"
    - "sequence to sequence learning as beam search optimization"
    - "adaptive neural compilation"
    - "programming with a differentiable forth interpreter"
    - "deepcoder learning to write programs"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-8 (induced by source paper "conditional neural processes"):
  with members:
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "variational memory addressing in generative models"
    - "prototypical networks for few shot learning"
    - "few shot autoregressive density estimation towards learning to learn distributions"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"
    - "one shot learning with memory augmented neural networks"
    - "conditional image generation with pixelcnn decoders"

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)