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: "a convolutional attention network for extreme summarization of source code"
  Abstract: Attention mechanisms in neural networks have proved useful for problems in which the input and output do not have fixed dimension. Often there exist features that are locally translation invariant and would be valuable for directing the model's attention, but previous attentional architectures are not constructed to learn such features specifically. We introduce an attentional neural network that employs convolution on the input tokens to detect local time-invariant and long-range topical attention features in a context-dependent way. We apply this architecture to the problem of extreme summarization of source code snippets into short, descriptive function name-like summaries. Using those features, the model sequentially generates a summary by marginalizing over two attention mechanisms: one that predicts the next summary token based on the attention weights of the input tokens and another that is able to copy a code token as-is directly into the summary. We demonstrate our convolutional attention neural network's performance on 10 popular Java projects showing that it achieves better performance compared to previous attentional mechanisms.

Connected through hyperedge HE-1 (induced by source paper "big code big vocabulary open vocabulary models for source code"):
  with members:
    - "modeling vocabulary for big code machine learning"
    - "on learning meaningful code changes via neural machine translation"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "the adverse effects of code duplication in machine learning models of code"
    - "frage frequency agnostic word representation"
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "adversarial robustness for code"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "certified robustness to adversarial word substitutions"
    - "inferring javascript types using graph neural networks"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "universal transformers"
    - "adversarial attacks on graph neural networks via meta learning"
    - "structured neural summarization"
    - "code2vec learning distributed representations of code"

Connected through hyperedge HE-3 (induced by source paper "structural language models of code"):
  with members:
    - "learning neurosymbolic generative models via program synthesis"
    - "learning to infer program sketches"
    - "learning programmatic idioms for scalable semantic parsing"
    - "write execute assess program synthesis with a repl"
    - "structured neural summarization"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "semantic robustness of models of source code"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "coset a benchmark for evaluating neural program embeddings"
    - "codesearchnet challenge evaluating the state of semantic code search"
    - "adversarial examples for models of code"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "code2vec learning distributed representations of code"
    - "neural augmented static analysis of android communication"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-5 (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-6 (induced by source paper "improving the robustness to data inconsistency between training and testing for code completion by hierarchical language model"):
  with members:
    - "program language translation using a grammar driven tree to tree model"
    - "mapping language to code in programmatic context"
    - "code completion with neural attention and pointer networks"
    - "tree to tree neural networks for program translation"
    - "a syntactic neural model for general purpose code generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "learning continuous semantic representations of symbolic expressions"
    - "a deep language model for software code"

Connected through hyperedge HE-7 (induced by source paper "in nomine function naming functions in stripped binaries with neural networks"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "dire a neural approach to decompiled identifier naming"
    - "neural reverse engineering of stripped binaries"
    - "tree2tree neural translation model for learning source code changes"
    - "universal language model fine tuning for text classification"
    - "a deep tree based model for software defect prediction"
    - "code2seq generating sequences from structured representations of code"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "leveraging code generation to improve code retrieval and summarization via dual learning"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "dual supervised learning for natural language understanding and generation"
    - "jointly learning semantic parser and natural language generator via dual information maximization"
    - "same representation different attentions shareable sentence representation learning from multiple tasks"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "code2seq generating sequences from structured representations of code"
    - "dual supervised learning"

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.LG(Machine Learning)