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: "autofolding for source code summarization"
  Abstract: Developers spend much of their time reading and browsing source code, raising new opportunities for summarization methods. Indeed, modern code editors provide code  folding , which allows one to selectively hide blocks of code. However this is impractical to use as folding decisions must be made manually or based on simple rules. We introduce the  autofolding problem , which is to automatically create a code summary by folding less informative code regions. We present a novel solution by formulating the problem as a sequence of AST folding decisions, leveraging a scoped topic model for code tokens. On an annotated set of popular open source projects, we show that our summarizer outperforms simpler baselines, yielding a 28 percent error reduction. Furthermore, we find through a case study that our summarizer is strongly preferred by experienced developers. More broadly, we hope this work will aid program comprehension by turning code folding into a usable and valuable tool.

Connected through hyperedge HE-1 (induced by source paper "deep transfer learning for source code modeling"):
  with members:
    - "code2vec learning distributed representations of code"
    - "an exploration of dropout with rnns for natural language inference"
    - "attention is all you need"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "collective intelligence for smarter api recommendations in python"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deep api learning"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "generating summaries for methods of event driven programs an android case study"):
  with members:
    - "automatically generating commit messages from diffs using neural machine translation"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "a convolutional attention network for extreme summarization of source code"
    - "effective approaches to attention based neural machine translation"
    - "recommending insightful comments for source code using crowdsourced knowledge"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "adapting neural text classification for improved software categorization"):
  with members:
    - "a survey of machine learning for big code and naturalness"
    - "a brief survey of text mining classification clustering and extraction techniques"
    - "a neural architecture for generating natural language descriptions from source code changes"
    - "a survey of neural network techniques for feature extraction from text"
    - "deep api learning"
    - "character level convolutional networks for text classification"
    - "a c lstm neural network for text classification"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-4 (induced by source paper "a survey of machine learning for big code and naturalness"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "deep reinforcement learning for programming language correction"
    - "recent advances in neural program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "codesum translate program language to natural language"
    - "neural attribute machines for program generation"
    - "semantically enhanced software traceability using deep learning techniques"

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.SE(Software Engineering)