human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "grammar filtering for syntax guided synthesis"):
  Abstract: Programming-by-example (PBE) is a synthesis paradigm that allows users to generate functions by simply providing input-output examples. While a promising interaction paradigm, synthesis is still too slow for realtime interaction and more widespread adoption. Existing approaches to PBE synthesis have used automated reasoning tools, such as SMT solvers, as well as works applying machine learning techniques. At its core, the automated reasoning approach relies on highly domain specific knowledge of programming languages. On the other hand, the machine learning approaches utilize the fact that when working with program code, it is possible to generate arbitrarily large training datasets. In this work, we propose a system for using machine learning in tandem with automated reasoning techniques to solve Syntax Guided Synthesis (SyGuS) style PBE problems. By preprocessing SyGuS PBE problems with a neural network, we can use a data driven approach to reduce the size of the search space, then allow automated reasoning-based solvers to more quickly find a solution analytically. Our system is able to run atop existing SyGuS PBE synthesis tools, decreasing the runtime of the winner of the 2019 SyGuS Competition for the PBE Strings track by 47.65% to outperform all of the competing tools.

Includes members:
  Member "learning to infer program sketches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "structural language models of code")
  Member "synthetic datasets for neural program synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to represent programs with property signatures")
  Member "neural guided deductive search for real time program synthesis from examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "comparison of syntactic and semantic representations of programs in neural embeddings")
  Member "leveraging grammar and reinforcement learning for neural program synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving molecular design by stochastic iterative target augmentation")
  Member "robustfill neural program learning under noisy i o":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "sygus comp 2017 results and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "oasis ilp guided synthesis of loop invariants")
  Member "learning efficient algorithms with hierarchical attentive memory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "progress extrapolating algorithmic learning to arbitrary sequence lengths")
  Member "learning neural programs to parse programs":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)