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: "leveraging grammar and reinforcement learning for neural program synthesis"
  Abstract: Program synthesis is the task of automatically generating a program consistent with a specification. Recent years have seen proposal of a number of neural approaches for program synthesis, many of which adopt a sequence generation paradigm similar to neural machine translation, in which sequence-to-sequence models are trained to maximize the likelihood of known reference programs. While achieving impressive results, this strategy has two key limitations. First, it ignores Program Aliasing: the fact that many different programs may satisfy a given specification (especially with incomplete specifications such as a few input-output examples). By maximizing the likelihood of only a single reference program, it penalizes many semantically correct programs, which can adversely affect the synthesizer performance. Second, this strategy overlooks the fact that programs have a strict syntax that can be efficiently checked. To address the first limitation, we perform reinforcement learning on top of a supervised model with an objective that explicitly maximizes the likelihood of generating semantically correct programs. For addressing the second limitation, we introduce a training procedure that directly maximizes the probability of generating syntactically correct programs that fulfill the specification. We show that our contributions lead to improved accuracy of the models, especially in cases where the training data is limited.

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 "grammar filtering for syntax guided synthesis"):
  with members:
    - "learning to infer program sketches"
    - "synthetic datasets for neural program synthesis"
    - "neural guided deductive search for real time program synthesis from examples"
    - "robustfill neural program learning under noisy i o"
    - "sygus comp 2017 results and analysis"
    - "learning efficient algorithms with hierarchical attentive memory"
    - "learning neural programs to parse programs"
    - "deepcoder learning to write programs"

Connected through hyperedge HE-3 (induced by source paper "counterexample guided neural synthesis"):
  with members:
    - "neural program synthesis with priority queue training"
    - "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-4 (induced by source paper "towards modular algorithm induction"):
  with members:
    - "modular networks learning to decompose neural computation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "automatically composing representation transformations as a means for generalization"
    - "robustfill neural program learning under noisy i o"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "deepcoder learning to write programs"
    - "neural random access machines"
    - "learning simple algorithms from examples"

Connected through hyperedge HE-5 (induced by source paper "neural logic machines"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "deepproblog neural probabilistic logic programming"
    - "logical rule induction and theory learning using neural theorem proving"
    - "tunneling neural perception and logic reasoning through abductive learning"
    - "learning explanatory rules from noisy data"
    - "robustfill neural program learning under noisy i o"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "learning transferable graph exploration"):
  with members:
    - "synthetic datasets for neural program synthesis"
    - "dom q net grounded rl on structured language"
    - "learning a sat solver from single bit supervision"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "learning to search with mctsnets"
    - "meta reinforcement learning of structured exploration strategies"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-7 (induced by source paper "maybe deep neural networks are the best choice for modeling source code"):
  with members:
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "robustfill neural program learning under noisy i o"
    - "attention is all you need"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"
    - "a survey of machine learning for big code and naturalness"

Connected through hyperedge HE-8 (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"
    - "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"

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.LG(Machine Learning)