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 to represent programs with graphs"
  Abstract: Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax. For example, long-range dependencies induced by using the same variable or function in distant locations are often not considered. We propose to use graphs to represent both the syntactic and semantic structure of code and use graph-based deep learning methods to learn to reason over program structures. #R##N#In this work, we present how to construct graphs from source code and how to scale Gated Graph Neural Networks training to such large graphs. We evaluate our method on two tasks: VarNaming, in which a network attempts to predict the name of a variable given its usage, and VarMisuse, in which the network learns to reason about selecting the correct variable that should be used at a given program location. Our comparison to methods that use less structured program representations shows the advantages of modeling known structure, and suggests that our models learn to infer meaningful names and to solve the VarMisuse task in many cases. Additionally, our testing showed that VarMisuse identifies a number of bugs in mature open-source projects.

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"
    - "structured neural summarization"
    - "code2vec learning distributed representations of code"

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 "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 "adaptive propagation graph convolutional network"):
  with members:
    - "constant time graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "diffusion improves graph learning"
    - "fast graph representation learning with pytorch geometric"
    - "a gentle introduction to deep learning for graphs"
    - "a comprehensive survey on graph neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "weisfeiler and leman go neural higher order graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model"):
  with members:
    - "dire a neural approach to decompiled identifier naming"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "a comprehensive survey on graph neural networks"
    - "code2vec learning distributed representations of code"
    - "an empirical study on learning bug fixing patches in the wild via neural machine translation"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "opennmt open source toolkit for neural machine translation"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"

Connected through hyperedge HE-6 (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 python code suggestion with a sparse pointer network"

Connected through hyperedge HE-7 (induced by source paper "detecting code clones with graph neural networkand flow augmented abstract syntax tree"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "graph matching networks for learning the similarity of graph structured objects"
    - "graph neural networks a review of methods and applications"
    - "convolutional neural networks over control flow graphs for software defect prediction"
    - "oreo detection of clones in the twilight zone"
    - "generative code modeling with graphs"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-8 (induced by source paper "automating botnet detection with graph neural networks"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "pytorch an imperative style high performance deep learning library"
    - "graph neural networks a review of methods and applications"
    - "machine learning ddos detection for consumer internet of things devices"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"
    - "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.LG(Machine Learning)