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: "learning python code suggestion with a sparse pointer network"
  Abstract: To enhance developer productivity, all modern integrated development environments (IDEs) include code suggestion functionality that proposes likely next tokens at the cursor. While current IDEs work well for statically-typed languages, their reliance on type annotations means that they do not provide the same level of support for dynamic programming languages as for statically-typed languages. Moreover, suggestion engines in modern IDEs do not propose expressions or multi-statement idiomatic code. Recent work has shown that language models can improve code suggestion systems by learning from software repositories. This paper introduces a neural language model with a sparse pointer network aimed at capturing very long range dependencies. We release a large-scale code suggestion corpus of 41M lines of Python code crawled from GitHub. On this corpus, we found standard neural language models to perform well at suggesting local phenomena, but struggle to refer to identifiers that are introduced many tokens in the past. By augmenting a neural language model with a pointer network specialized in referring to predefined classes of identifiers, we obtain a much lower perplexity and a 5 percentage points increase in accuracy for code suggestion compared to an LSTM baseline. In fact, this increase in code suggestion accuracy is due to a 13 times more accurate prediction of identifiers. Furthermore, a qualitative analysis shows this model indeed captures interesting long-range dependencies, like referring to a class member defined over 60 tokens in the past.

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 "learning to fix build errors with graph2diff neural networks"):
  with members:
    - "harnessing evolution for multi hunk program repair"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "neural program repair by jointly learning to localize and repair"
    - "getafix learning to fix bugs automatically"
    - "a comprehensive survey on graph neural networks"
    - "generative code modeling with graphs"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "a self attentional neural architecture for code completion with multi task learning"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "code completion with neural attention and pointer networks"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "attention is all you need"
    - "an overview of multi task learning in deep neural networks"
    - "multi task domain adaptation for sequence tagging"

Connected through hyperedge HE-4 (induced by source paper "program classification using gated graph attention neural network for online programming service"):
  with members:
    - "a survey of machine learning for big code and naturalness"
    - "learning to represent programs with graphs"
    - "cross language learning for program classification using bilateral tree based convolutional neural networks"
    - "gated graph sequence neural networks"
    - "how deep is knowledge tracing"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep api learning"

Connected through hyperedge HE-5 (induced by source paper "learning semantic vector representations of source code via a siamese neural network"):
  with members:
    - "code2vec learning distributed representations of code"
    - "oreo detection of clones in the twilight zone"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "a deep tree based model for software defect prediction"
    - "attention is all you need"
    - "facenet a unified embedding for face recognition and clustering"
    - "adam a method for stochastic optimization"
    - "tbcnn a tree based convolutional neural network for programming language processing"

Connected through hyperedge HE-6 (induced by source paper "neural program synthesis with priority queue training"):
  with members:
    - "robustfill neural program learning under noisy i o"
    - "inferring and executing programs for visual reasoning"
    - "making neural programming architectures generalize via recursion"
    - "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
    - "learning to reason end to end module networks for visual question answering"
    - "pix2code generating code from a graphical user interface screenshot"
    - "neural sketch learning for conditional program generation"
    - "abstract syntax networks for code generation and semantic parsing"

Connected through hyperedge HE-7 (induced by source paper "neural networks for modeling source code edits"):
  with members:
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "progressive growing of gans for improved quality stability and variation"
    - "attention is all you need"
    - "adapting sequence models for sentence correction"
    - "a survey of machine learning for big code and naturalness"
    - "iterative refinement for machine translation"
    - "enhanced neural machine translation by learning from draft"
    - "pre translation for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "code completion with neural attention and pointer networks"):
  with members:
    - "pointing the unknown words"
    - "long short term memory networks for machine reading"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "latent predictor networks for code generation"
    - "recurrent memory network for language modeling"
    - "pointer sentinel mixture models"
    - "a convolutional attention network for extreme summarization of source code"
    - "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.NE(Neural and Evolutionary Computing)