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: "deepam migrate apis with multi modal sequence to sequence learning"
  Abstract: Computer programs written in one language are often required to be ported to other languages to support multiple devices and environments. When programs use language specific APIs (Application Programming Interfaces), it is very challenging to migrate these APIs to the corresponding APIs written in other languages. Existing approaches mine API mappings from projects that have corresponding versions in two languages. They rely on the sparse availability of bilingual projects, thus producing a limited number of API mappings. In this paper, we propose an intelligent system called DeepAM for automatically mining API mappings from a large-scale code corpus without bilingual projects. The key component of DeepAM is based on the multimodal sequence to sequence learning architecture that aims to learn joint semantic representations of bilingual API sequences from big source code data. Experimental results indicate that DeepAM significantly increases the accuracy of API mappings as well as the number of API mappings, when compared with the state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "big code big vocabulary open vocabulary models for source code"):
  with members:
    - "modeling vocabulary for big code machine learning"
    - "on learning meaningful code changes via neural machine translation"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "the adverse effects of code duplication in machine learning models of code"
    - "frage frequency agnostic word representation"
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "modeling vocabulary for big code machine learning"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "code2vec learning distributed representations of code"
    - "neural code comprehension a learnable representation of code semantics"
    - "a general path based representation for predicting program properties"
    - "the adverse effects of code duplication in machine learning models of code"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "treecaps tree structured capsule networks for program source code processing"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "textcaps handwritten character recognition with very small datasets"
    - "code2vec learning distributed representations of code"
    - "investigating capsule networks with dynamic routing for text classification"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "tree to tree neural networks for program translation"
    - "dynamic routing between capsules"

Connected through hyperedge HE-4 (induced by source paper "sar learning cross language api mappings with little knowledge"):
  with members:
    - "hierarchical learning of cross language mappings through distributed vector representations for code"
    - "transferring gans generating images from limited data"
    - "a survey of machine learning for big code and naturalness"
    - "bilateral multi perspective matching for natural language sentences"
    - "word translation without parallel data"
    - "deep api learning"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "an empirical study on learning bug fixing patches in the wild via neural machine translation"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "the codrep machine learning on source code competition"
    - "the adverse effects of code duplication in machine learning models of code"
    - "automatic software repair a bibliography"
    - "deep reinforcement learning for programming language correction"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "machine learning based prototyping of graphical user interfaces for mobile apps"

Connected through hyperedge HE-6 (induced by source paper "hierarchical learning of cross language mappings through distributed vector representations for code"):
  with members:
    - "deep api learning"
    - "sequence to sequence learning with neural networks"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-7 (induced by source paper "cross language learning for program classification using bilateral tree based convolutional neural networks"):
  with members:
    - "deep api learning"
    - "a large annotated corpus for learning natural language inference"
    - "convolutional neural networks over tree structures for programming language processing"
    - "building program vector representations for deep learning"
    - "efficient estimation of word representations in vector space"

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)