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: "structured neural summarization"
  Abstract: Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text. In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.

Connected through hyperedge HE-1 (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"
    - "code2vec learning distributed representations of code"
    - "adversarial attack on graph structured data"

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"
    - "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"
    - "the adverse effects of code duplication in machine learning models of code"

Connected through hyperedge HE-3 (induced by source paper "teaching temporal logics to neural networks"):
  with members:
    - "mathematical reasoning in latent space"
    - "guiding high performance sat solvers with unsat core predictions"
    - "enhancing the transformer with explicit relational encoding for math problem solving"
    - "analysing mathematical reasoning abilities of neural models"
    - "deep learning for symbolic mathematics"
    - "graph representations for higher order logic and theorem proving"
    - "holist an environment for machine learning of higher order theorem proving"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-4 (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"
    - "open vocabulary learning on source code with a graph structured cache"
    - "code2vec learning distributed representations of code"

Connected through hyperedge HE-5 (induced by source paper "neural reverse engineering of stripped binaries"):
  with members:
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "the adverse effects of code duplication in machine learning models of code"
    - "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-6 (induced by source paper "hierarchical transformers for multi document summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "generating wikipedia by summarizing long sequences"
    - "towards dynamic computation graphs via sparse latent structure"
    - "bottom up abstractive summarization"
    - "automatic detection of vague words and sentences in privacy policies"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "associating natural language comment and source code entities"):
  with members:
    - "wikiatomicedits a multilingual corpus of wikipedia edits for modeling language and discourse"
    - "the adverse effects of code duplication in machine learning models of code"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "typesql knowledge based type aware neural text to sql generation"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "retrieval based neural code generation"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-8 (induced by source paper "codesearchnet challenge evaluating the state of semantic code search"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "when deep learning met code search"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "the adverse effects of code duplication in machine learning models of code"
    - "mapping language to code in programmatic context"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "code2seq generating sequences from structured representations of code"

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)