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 guided deductive search for real time program synthesis from examples"
  Abstract: Synthesizing user-intended programs from a small number of input-output examples is a challenging problem with several important applications like spreadsheet manipulation, data wrangling and code refactoring. Existing synthesis systems either completely rely on deductive logic techniques that are extensively hand-engineered or on purely statistical models that need massive amounts of data, and in general fail to provide real-time synthesis on challenging benchmarks. In this work, we propose Neural Guided Deductive Search (NGDS), a hybrid synthesis technique that combines the best of both symbolic logic techniques and statistical models. Thus, it produces programs that satisfy the provided specifications by construction and generalize well on unseen examples, similar to data-driven systems. Our technique effectively utilizes the deductive search framework to reduce the learning problem of the neural component to a simple supervised learning setup. Further, this allows us to both train on sparingly available real-world data and still leverage powerful recurrent neural network encoders. We demonstrate the effectiveness of our method by evaluating on real-world customer scenarios by synthesizing accurate programs with up to 12x speed-up compared to state-of-the-art systems.

Connected through hyperedge HE-1 (induced by source paper "comparison of syntactic and semantic representations of programs in neural embeddings"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "code2vec learning distributed representations of code"
    - "naps natural program synthesis dataset"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "neural program search solving programming tasks from description and examples"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "neural guided constraint logic programming for program synthesis"
    - "program synthesis using conflict driven learning"

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"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "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 "learning to represent programs with property signatures"):
  with members:
    - "learning to infer program sketches"
    - "synthetic datasets for neural program synthesis"
    - "write execute assess program synthesis with a repl"
    - "code2vec learning distributed representations of code"
    - "the three pillars of machine programming"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "robustfill neural program learning under noisy i o"
    - "a survey of machine learning for big code and naturalness"

Connected through hyperedge HE-4 (induced by source paper "probabilistic neural symbolic models for interpretable visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "evalai towards better evaluation systems for ai agents"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "houdini lifelong learning as program synthesis"
    - "compositional attention networks for machine reasoning"

Connected through hyperedge HE-5 (induced by source paper "learning execution through neural code fusion"):
  with members:
    - "a neural network prefetcher for arbitrary memory access patterns"
    - "neural code comprehension a learnable representation of code semantics"
    - "learning memory access patterns"
    - "path based function embedding and its application to specification mining"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "ithemal accurate portable and fast basic block throughput estimation using deep neural networks"
    - "neural arithmetic logic units"
    - "automatic program synthesis of long programs with a learned garbage collector"

Connected through hyperedge HE-6 (induced by source paper "learning to infer program sketches"):
  with members:
    - "ain t nobody got time for coding structure aware program synthesis from natural language"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "neural program search solving programming tasks from description and examples"
    - "coarse to fine decoding for neural semantic parsing"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "neural guided constraint logic programming for program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "neural sketch learning for conditional program generation"

Connected through hyperedge HE-7 (induced by source paper "write execute assess program synthesis with a repl"):
  with members:
    - "learning to infer program sketches"
    - "learning to infer and execute 3d shape programs"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "program synthesis through reinforcement learning guided tree search"
    - "selecting representative examples for program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "csgnet neural shape parser for constructive solid geometry"

Connected through hyperedge HE-8 (induced by source paper "neural guided symbolic regression with asymptotic constraints"):
  with members:
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "learning equations for extrapolation and control"
    - "robustfill neural program learning under noisy i o"
    - "deepcoder learning to write programs"
    - "neuro symbolic program synthesis"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)