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: "deepsoft a vision for a deep model of software"
  Abstract: Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling software and its development process to predict future risks and recommend interventions. DeepSoft, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term temporal dependencies that occur in software evolution. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and automatically generating code patches from bug reports.

Connected through hyperedge HE-1 (induced by source paper "on learning meaningful code changes via neural machine translation"):
  with members:
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "semantically enhanced software traceability using deep learning techniques"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "massive exploration of neural machine translation architectures"
    - "learn fuzz machine learning for input fuzzing"
    - "a deep learning model for estimating story points"
    - "vocabulary manipulation for neural machine translation"
    - "deep api learning"

Connected through hyperedge HE-2 (induced by source paper "v fuzz vulnerability oriented evolutionary fuzzing"):
  with members:
    - "deep reinforcement fuzzing"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "angora efficient fuzzing by principled search"
    - "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
    - "automatic feature learning for vulnerability prediction"
    - "faster fuzzing reinitialization with deep neural models"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "not all bytes are equal neural byte sieve for fuzzing"

Connected through hyperedge HE-3 (induced by source paper "explainable software analytics"):
  with members:
    - "explanation in artificial intelligence insights from the social sciences"
    - "abstract syntax networks for code generation and semantic parsing"
    - "easy over hard a case study on deep learning"
    - "a deep learning model for estimating story points"
    - "column networks for collective classification"
    - "understanding deep learning requires rethinking generalization"
    - "generating visual explanations"
    - "the mythos of model interpretability"

Connected through hyperedge HE-4 (induced by source paper "a deep tree based model for software defect prediction"):
  with members:
    - "a deep language model for software code"
    - "deep api learning"
    - "improved semantic representations from tree structured long short term memory networks"
    - "on the naturalness of buggy code"

Connected through hyperedge HE-5 (induced by source paper "automatic feature learning for vulnerability prediction"):
  with members:
    - "a deep language model for software code"
    - "deep api learning"

Connected through hyperedge HE-6 (induced by source paper "a deep learning model for estimating story points"):
  with members:
    - "a deep language model for software code"
    - "why deep neural networks for function approximation"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep api learning"
    - "theano a python framework for fast computation of mathematical expressions"
    - "deep learning in neural networks"
    - "beyond short snippets deep networks for video classification"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-7 (induced by source paper "a deep language model for software code"):
  with members:
    - "convolutional neural networks over tree structures for programming language processing"
    - "advances in optimizing recurrent networks"

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)