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: "end to end prediction of buffer overruns from raw source code via neural memory networks"
  Abstract: Detecting buffer overruns from a source code is one of the most common and yet challenging tasks in program analysis. Current approaches have mainly relied on rigid rules and handcrafted features devised by a few experts, limiting themselves in terms of flexible applicability and robustness due to diverse bug patterns and characteristics existing in sophisticated real-world software programs. In this paper, we propose a novel, data-driven approach that is completely end-to-end without requiring any hand-crafted features, thus free from any program language-specific structural limitations. In particular, our approach leverages a recently proposed neural network model called memory networks that have shown the state-of-the-art performances mainly in question-answering tasks. Our experimental results using source codes demonstrate that our proposed model is capable of accurately detecting simple buffer overruns. We also present in-depth analyses on how a memory network can learn to understand the semantics in programming languages solely from raw source codes, such as tracing variables of interest, identifying numerical values, and performing their quantitative comparisons.

Connected through hyperedge HE-1 (induced by source paper "neutaint efficient dynamic taint analysis with neural networks"):
  with members:
    - "neuro symbolic execution the feasibility of an inductive approach to symbolic execution"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "smartseed smart seed generation for efficient fuzzing"
    - "neuzz efficient fuzzing with neural program smoothing"
    - "angora efficient fuzzing by principled search"
    - "robustfill neural program learning under noisy i o"
    - "axiomatic attribution for deep networks"
    - "faster fuzzing reinitialization with deep neural models"

Connected through hyperedge HE-2 (induced by source paper "neural bug finding a study of opportunities and challenges"):
  with members:
    - "modeling vocabulary for big code machine learning"
    - "neural program repair by jointly learning to localize and repair"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "learning to represent edits"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "generative code modeling with graphs"
    - "path based function embedding and its application to specification mining"

Connected through hyperedge HE-3 (induced by source paper "prnn recurrent neural network with persistent memory"):
  with members:
    - "chinese poetry generation with a working memory model"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "attention is all you need"
    - "deepstory video story qa by deep embedded memory networks"
    - "recurrent highway networks"
    - "pointer sentinel mixture models"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-4 (induced by source paper "deepbugs a learning approach to name based bug detection"):
  with members:
    - "a general path based representation for predicting program properties"
    - "neural attribute machines for program generation"
    - "smartpaste learning to adapt source code"
    - "a survey of machine learning for big code and naturalness"
    - "learning to represent programs with graphs"
    - "finding likely errors with bayesian specifications"
    - "learn fuzz machine learning for input fuzzing"
    - "automated correction for syntax errors in programming assignments using recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "towards security defect prediction with ai"):
  with members:
    - "automated vulnerability detection in source code using deep representation learning"
    - "neural arithmetic logic units"
    - "a survey of machine learning for big code and naturalness"
    - "a survey of symbolic execution techniques"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

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)