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: "dynamic neural program embedding for program repair"
  Abstract: Neural program embeddings have shown much promise recently for a variety of program analysis tasks, including program synthesis, program repair, fault localization, etc. However, most existing program embeddings are based on syntactic features of programs, such as raw token sequences or abstract syntax trees. Unlike images and text, a program has an unambiguous semantic meaning that can be difficult to capture by only considering its syntax (i.e. syntactically similar pro- grams can exhibit vastly different run-time behavior), which makes syntax-based program embeddings fundamentally limited. This paper proposes a novel semantic program embedding that is learned from program execution traces. Our key insight is that program states expressed as sequential tuples of live variable values not only captures program semantics more precisely, but also offer a more natural fit for Recurrent Neural Networks to model. We evaluate different syntactic and semantic program embeddings on predicting the types of errors that students make in their submissions to an introductory programming class and two exercises on the CodeHunt education platform. Evaluation results show that our new semantic program embedding significantly outperforms the syntactic program embeddings based on token sequences and abstract syntax trees. In addition, we augment a search-based program repair system with the predictions obtained from our se- mantic embedding, and show that search efficiency is also significantly improved.

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"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "code2seq generating sequences from structured representations of code"

Connected through hyperedge HE-2 (induced by source paper "comparison of syntactic and semantic representations of programs in neural embeddings"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "code2vec learning distributed representations of code"
    - "naps natural program synthesis dataset"
    - "neural guided deductive search for real time program synthesis from examples"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "neural program search solving programming tasks from description and examples"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "neural guided constraint logic programming for program synthesis"

Connected through hyperedge HE-3 (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-4 (induced by source paper "learning to encode and classify test executions"):
  with members:
    - "code2vec learning distributed representations of code"
    - "code2seq generating sequences from structured representations of code"
    - "learning to represent programs with graphs"
    - "a convolutional attention network for extreme summarization of source code"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (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"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "learning to generate corrective patches using neural machine translation"

Connected through hyperedge HE-6 (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"
    - "end to end prediction of buffer overruns from raw source code via neural memory networks"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-7 (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"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "code2seq generating sequences from structured representations of code"
    - "attention is all you need"
    - "learning to represent programs with graphs"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-8 (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"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "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"

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.AI(Artificial Intelligence)