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 "codegru context aware deep learning with gated recurrent unit for source code modeling"):
  Abstract: Recently many NLP-based deep learning models have been applied to model source code for source code suggestion and recommendation tasks. A major limitation of these approaches is that they take source code as simple tokens of text and ignore its contextual, syntaxtual and structural dependencies. In this work, we present CodeGRU, a Gated Recurrent Unit based source code language model that is capable of capturing contextual, syntaxtual and structural dependencies for modeling the source code. The CodeGRU introduces the following several new components. The Code Sampler is first proposed for selecting noise-free code samples and transforms obfuscate code to its proper syntax, which helps to capture syntaxtual and structural dependencies. The Code Regularize is next introduced to encode source code which helps capture the contextual dependencies of the source code. Finally, we propose a novel method which can learn variable size context for modeling source code. We evaluated CodeGRU with real-world dataset and it shows that CodeGRU can effectively capture contextual, syntaxtual and structural dependencies which previous works fails. We also discuss and visualize two use cases of CodeGRU for source code modeling tasks (1) source code suggestion, and (2) source code generation.

Includes members:
  Member "an exploration of dropout with rnns for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep transfer learning for source code modeling")
  Member "abstract syntax networks for code generation and semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "schema2qa answering complex queries on the structured web with a neural model")
  Member "collective intelligence for smarter api recommendations in python":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep transfer learning for source code modeling")
  Member "deep api learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "a convolutional attention network for extreme summarization of source code":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "word2vec parameter learning explained":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "mining idioms from source code":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "program synthesis and semantic parsing with learned code idioms")

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.NE(Neural and Evolutionary Computing)