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 constraint logic programming for program synthesis"
  Abstract: Synthesizing programs using example input/outputs is a classic problem in artificial intelligence. We present a method for solving Programming By Example (PBE) problems by using a neural model to guide the search of a constraint logic programming system called miniKanren. Crucially, the neural model uses miniKanren's internal representation as input; miniKanren represents a PBE problem as recursive constraints imposed by the provided examples. We explore Recurrent Neural Network and Graph Neural Network models. We contribute a modified miniKanren, drivable by an external agent, available at this https URL We show that our neural-guided approach using constraints can synthesize programs faster in many cases, and importantly, can generalize to larger problems.

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"
    - "neural guided deductive search for real time program synthesis from examples"
    - "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"
    - "program synthesis using conflict driven learning"

Connected through hyperedge HE-2 (induced by source paper "neurally guided structure inference"):
  with members:
    - "learning to infer and execute 3d shape programs"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "robust text to sql generation with execution guided decoding"
    - "tree to tree neural networks for program translation"
    - "robustfill neural program learning under noisy i o"
    - "making neural programming architectures generalize via recursion"
    - "neural sketch learning for conditional program generation"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "neural probabilistic logic programming in deepproblog"):
  with members:
    - "neural logic machines"
    - "playgol learning programs through play"
    - "nlprolog reasoning with weak unification for question answering in natural language"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"
    - "learning explanatory rules from noisy data"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-4 (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"
    - "neural guided deductive search for real time program synthesis from examples"
    - "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"
    - "robustfill neural program learning under noisy i o"
    - "neural sketch learning for conditional program generation"

Connected through hyperedge HE-5 (induced by source paper "towards neural guided program synthesis for linear temporal logic specifications"):
  with members:
    - "the 5th reactive synthesis competition syntcomp 2018 benchmarks participants results"
    - "towards synthesizing complex programs from input output examples"
    - "deepcoder learning to write programs"
    - "neuro symbolic program synthesis"
    - "terpret a probabilistic programming language for program induction"
    - "prioritized experience replay"
    - "neural turing machines"

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)