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 "spoc search based pseudocode to code"):
  Abstract: We consider the task of mapping pseudocode to long programs that are functionally correct. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the pseudocode to find a program that passes the validation. However, without proper credit assignment to localize the sources of program failures, it is difficult to guide search toward more promising programs. We propose to perform credit assignment based on signals from compilation errors, which constitute 88.7% of program failures. Concretely, we treat the translation of each pseudocode line as a discrete portion of the program, and whenever a synthesized program fails to compile, an error localization method tries to identify the portion of the program responsible for the failure. We then focus search over alternative translations of the pseudocode for those portions. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 programs with human-authored pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the pseudocode from 25.6% to 44.7%.

Includes members:
  Member "neural program repair by jointly learning to localize and repair":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to fix build errors with graph2diff neural networks")
  Member "frangel component based synthesis with control structures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incremental sampling without replacement for sequence models")
  Member "seq2sql generating structured queries from natural language using reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "nl2bash a corpus and semantic parser for natural language interface to the linux operating system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "clai a platform for ai skills on the command line")
  Member "naps natural program synthesis dataset":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "comparison of syntactic and semantic representations of programs in neural embeddings")
  Member "mapping language to code in programmatic context":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structural language models of code")
  Member "coarse to fine decoding for neural semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "structural language models of code")
  Member "retrieval based neural code generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "editing based sql query generation for cross domain context dependent questions")

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)