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: "the adverse effects of code duplication in machine learning models of code"
  Abstract: The field of big code relies on mining large corpora of code to perform some learning task. A significant threat to this approach has been recently identified by Lopes et al. (2017) who found a large amount of near-duplicate code on GitHub. However, the impact of code duplication has not been noticed by researchers devising machine learning models for source code. In this work, we explore the effects of code duplication on machine learning models showing that reported performance metrics are sometimes inflated by up to 100% when testing on duplicated code corpora compared to the performance on de-duplicated corpora which more accurately represent how machine learning models of code are used by software engineers. We present a duplication index for widely used datasets, list best practices for collecting code corpora and evaluating machine learning models on them. Finally, we release tools to help the community avoid this problem in future research.

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"
    - "frage frequency agnostic word representation"
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (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-3 (induced by source paper "what you see is what it means semantic representation learning of code based on visualization and transfer learning"):
  with members:
    - "code2vec learning distributed representations of code"
    - "oreo detection of clones in the twilight zone"
    - "neural code comprehension a learnable representation of code semantics"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "code2seq generating sequences from structured representations of code"
    - "look listen and learn"
    - "deep residual learning for image recognition"
    - "convolutional neural networks over tree structures for programming language processing"

Connected through hyperedge HE-4 (induced by source paper "a dataset for github repository deduplication"):
  with members:
    - "a complete set of related git repositories identified via community detection approaches based on shared commits"
    - "usage and attribution of stack overflow code snippets in github projects"
    - "topic modeling of public repositories at scale using names in source code"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "on the statistical detection of adversarial examples"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "a convolutional attention network for extreme summarization of source code"
    - "structured generative models of natural source code"

Connected through hyperedge HE-5 (induced by source paper "modeling vocabulary for big code machine learning"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "code2vec learning distributed representations of code"
    - "neural code comprehension a learnable representation of code semantics"
    - "a general path based representation for predicting program properties"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep rnns encode soft hierarchical syntax"

Connected through hyperedge HE-6 (induced by source paper "structural language models for any code generation"):
  with members:
    - "structural language models of code"
    - "learning neurosymbolic generative models via program synthesis"
    - "learning to infer program sketches"
    - "learning programmatic idioms for scalable semantic parsing"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "write execute assess program synthesis with a repl"
    - "structured neural summarization"
    - "open vocabulary learning on source code with a graph structured cache"

Connected through hyperedge HE-7 (induced by source paper "neural reverse engineering of stripped binaries"):
  with members:
    - "structured neural summarization"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "mapping language to code in programmatic context"
    - "generative code modeling with graphs"
    - "tree to tree neural networks for program translation"
    - "code2seq generating sequences from structured representations of code"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "pre trained contextual embedding of source code"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "neural program repair by jointly learning to localize and repair"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "when deep learning met code search"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep learning to detect redundant method comments"
    - "code2vec learning distributed representations of code"
    - "self attention with relative position representations"

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)