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 synthesis with priority queue training"
  Abstract: We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated programs so far. Then, we synthesize new programs and add them to the priority queue by sampling from the RNN. We benchmark our algorithm, called priority queue training (or PQT), against genetic algorithm and reinforcement learning baselines on a simple but expressive Turing complete programming language called BF. Our experimental results show that our simple PQT algorithm significantly outperforms the baselines. By adding a program length penalty to the reward function, we are able to synthesize short, human readable programs.

Connected through hyperedge HE-1 (induced by source paper "counterexample guided neural synthesis"):
  with members:
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "programming not only by example"
    - "deepcoder learning to write programs"
    - "automated synthesis of semantic malware signatures using maximum satisfiability"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "neuro symbolic program synthesis"
    - "neural gpus learn algorithms"

Connected through hyperedge HE-2 (induced by source paper "learning to generalize from sparse and underspecified rewards"):
  with members:
    - "self supervised generalisation with meta auxiliary learning"
    - "reward shaping via meta learning"
    - "on first order meta learning algorithms"
    - "multi task maximum entropy inverse reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "discourse aware neural rewards for coherent text generation"
    - "babyai first steps towards grounded language learning with a human in the loop"

Connected through hyperedge HE-3 (induced by source paper "a discrete hard em approach for weakly supervised question answering"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "learning to generalize from sparse and underspecified rewards"
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "compositional questions do not necessitate multi hop reasoning"
    - "multi style generative reading comprehension"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "x sql reinforce schema representation with context"
    - "a bert baseline for the natural questions"

Connected through hyperedge HE-4 (induced by source paper "modularity as a means for complexity management in neural networks learning"):
  with members:
    - "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"
    - "neural program meta induction"
    - "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-5 (induced by source paper "deep symbolic regression recovering mathematical expressions from data via risk seeking policy gradients"):
  with members:
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "learning equations for extrapolation and control"
    - "attention is all you need"
    - "searching for activation functions"
    - "neural optimizer search with reinforcement learning"
    - "swish a self gated activation function"

Connected through hyperedge HE-6 (induced by source paper "transfer nas knowledge transfer between search spaces with transformer agents"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural architecture search over a graph search space"
    - "transfer learning with neural automl"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-7 (induced by source paper "memory augmented policy optimization for program synthesis and semantic parsing"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "semantic parsing with syntax and table aware sql generation"
    - "typesql knowledge based type aware neural text to sql generation"
    - "it was the training data pruning too"

Connected through hyperedge HE-8 (induced by source paper "evolutionary neural hybrid agents for architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "reinforced evolutionary neural architecture search"
    - "autoaugment learning augmentation policies from data"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "efficient neural architecture search via parameter sharing"

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)