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: "building program vector representations for deep learning"
  Abstract: Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.

Connected through hyperedge HE-1 (induced by source paper "modeling programs hierarchically with stack augmented lstm"):
  with members:
    - "maybe deep neural networks are the best choice for modeling source code"
    - "open vocabulary learning on source code with a graph structured cache"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "code completion with neural attention and pointer networks"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "attention is all you need"
    - "learning to represent programs with graphs"

Connected through hyperedge HE-2 (induced by source paper "checking smart contracts with structural code embedding"):
  with members:
    - "automating app review response generation"
    - "smartembed a tool for clone and bug detection in smart contracts through structural code embedding"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "a general path based representation for predicting program properties"
    - "a survey on the security of blockchain systems"
    - "blockchain based smart contracts a systematic mapping study"
    - "under optimized smart contracts devour your money"
    - "an empirical analysis of smart contracts platforms applications and design patterns"

Connected through hyperedge HE-3 (induced by source paper "recommendation of move method refactoring using path based representation of code"):
  with members:
    - "on the feasibility of transfer learning code smells using deep learning"
    - "a machine learning based ensemble method for anti patterns detection"
    - "code2vec learning distributed representations of code"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "encore ensemble learning using convolution neural machine translation for automatic program repair"):
  with members:
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "code2vec learning distributed representations of code"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "fixminer mining relevant fix patterns for automated program repair"
    - "tree2tree neural translation model for learning source code changes"

Connected through hyperedge HE-5 (induced by source paper "code vectors understanding programs through embedded abstracted symbolic traces"):
  with members:
    - "code2vec learning distributed representations of code"
    - "neural code comprehension a learnable representation of code semantics"
    - "a general path based representation for predicting program properties"
    - "path based function embedding and its application to specification mining"
    - "a survey of machine learning for big code and naturalness"
    - "learning to represent programs with graphs"
    - "dynamic neural program embedding for program repair"
    - "learning continuous semantic representations of symbolic expressions"

Connected through hyperedge HE-6 (induced by source paper "vuldeepecker a deep learning based system for vulnerability detection"):
  with members:
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "sourcerercc scaling code clone detection to big code"
    - "lattice based recurrent neural network encoders for neural machine translation"
    - "deep api learning"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-7 (induced by source paper "ast based deep learning for detecting malicious powershell"):
  with members:
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "detecting malicious powershell commands using deep neural networks"
    - "on visual hallmarks of robustness to adversarial malware"

Connected through hyperedge HE-8 (induced by source paper "mining fix patterns for findbugs violations"):
  with members:
    - "fixminer mining relevant fix patterns for automated program repair"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "learning quick fixes from code repositories"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "a survey of machine learning for big code and naturalness"
    - "cross language learning for program classification using bilateral tree based convolutional neural networks"
    - "learning syntactic program transformations from examples"

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)