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: "code vectors understanding programs through embedded abstracted symbolic traces"
  Abstract: With the rise of machine learning, there is a great deal of interest in treating programs as data to be fed to learning algorithms. However, programs do not start off in a form that is immediately amenable to most off-the-shelf learning techniques. Instead, it is necessary to transform the program to a suitable representation before a learning technique can be applied.     In this paper, we use abstractions of traces obtained from symbolic execution of a program as a representation for learning word embeddings. We trained a variety of word embeddings under hundreds of parameterizations, and evaluated each learned embedding on a suite of different tasks. In our evaluation, we obtain 93% top-1 accuracy on a benchmark consisting of over 19,000 API-usage analogies extracted from the Linux kernel. In addition, we show that embeddings learned from (mainly) semantic abstractions provide nearly triple the accuracy of those learned from (mainly) syntactic abstractions.

Connected through hyperedge HE-1 (induced by source paper "cc2vec distributed representations of code changes"):
  with members:
    - "patchnet hierarchical deep learning based stable patch identification for the linux kernel"
    - "semantic source code models using identifier embeddings"
    - "a neural model for generating natural language summaries of program subroutines"
    - "import2vec learning embeddings for software libraries"
    - "code2vec learning distributed representations of code"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "code2seq generating sequences from structured representations of code"
    - "automatically generating commit messages from diffs using neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "defense methods against adversarial examples for recurrent neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating natural language adversarial examples"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "robust neural malware detection models for emulation sequence learning"
    - "stochastic substitute training a gray box approach to craft adversarial examples against gradient obfuscation defenses"

Connected through hyperedge HE-3 (induced by source paper "sequencer sequence to sequence learning for end to end program repair"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "the codrep machine learning on source code competition"
    - "an empirical study of fault localization families and their combinations"
    - "automatic software repair a bibliography"
    - "learning to generate corrective patches using neural machine translation"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"

Connected through hyperedge HE-4 (induced by source paper "learning blended precise semantic program embeddings"):
  with members:
    - "coset a benchmark for evaluating neural program embeddings"
    - "learning scalable and precise representation of program semantics"
    - "code2vec learning distributed representations of code"
    - "code2seq generating sequences from structured representations of code"
    - "attention is all you need"
    - "learning to represent programs with graphs"
    - "dynamic neural program embedding for program repair"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning execution through neural code fusion"):
  with members:
    - "a neural network prefetcher for arbitrary memory access patterns"
    - "neural code comprehension a learnable representation of code semantics"
    - "learning memory access patterns"
    - "path based function embedding and its application to specification mining"
    - "ithemal accurate portable and fast basic block throughput estimation using deep neural networks"
    - "neural arithmetic logic units"
    - "neural guided deductive search for real time program synthesis from examples"
    - "automatic program synthesis of long programs with a learned garbage collector"

Connected through hyperedge HE-6 (induced by source paper "evaluating semantic representations of source code"):
  with members:
    - "coset a benchmark for evaluating neural program embeddings"
    - "a literature study of embeddings on source code"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "path based function embedding and its application to specification mining"
    - "automated software vulnerability detection with machine learning"
    - "code2seq generating sequences from structured representations of code"
    - "semantic code repair using neuro symbolic transformation networks"

Connected through hyperedge HE-7 (induced by source paper "exploiting token and path based representations of code for identifying security relevant commits"):
  with members:
    - "a manually curated dataset of fixes to vulnerabilities of open source software"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "dropblock a regularization method for convolutional networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "beyond metadata code centric and usage based analysis of known vulnerabilities in open source software"
    - "code2seq generating sequences from structured representations of code"

Connected through hyperedge HE-8 (induced by source paper "learning the relation between code features and code transforms with structured prediction"):
  with members:
    - "sequencer sequence to sequence learning for end to end program repair"
    - "alleviating patch overfitting with automatic test generation a study of feasibility and effectiveness for the nopol repair system"
    - "towards an automated approach for bug fix pattern detection"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "automatic software repair a bibliography"
    - "path based function embedding and its application to specification mining"
    - "learning to generate corrective patches using neural machine translation"

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)