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: "semantic code repair using neuro symbolic transformation networks"
  Abstract: We study the problem of semantic code repair, which can be broadly defined as automatically fixing non-syntactic bugs in source code. The majority of past work in semantic code repair assumed access to unit tests against which candidate repairs could be validated. In contrast, the goal here is to develop a strong statistical model to accurately predict both bug locations and exact fixes without access to information about the intended correct behavior of the program. Achieving such a goal requires a robust contextual repair model, which we train on a large corpus of real-world source code that has been augmented with synthetically injected bugs. Our framework adopts a two-stage approach where first a large set of repair candidates are generated by rule-based processors, and then these candidates are scored by a statistical model using a novel neural network architecture which we refer to as Share, Specialize, and Compete. Specifically, the architecture (1) generates a shared encoding of the source code using an RNN over the abstract syntax tree, (2) scores each candidate repair using specialized network modules, and (3) then normalizes these scores together so they can compete against one another in comparable probability space. We evaluate our model on a real-world test set gathered from GitHub containing four common categories of bugs. Our model is able to predict the exact correct repair 41\% of the time with a single guess, compared to 13\% accuracy for an attentional sequence-to-sequence model.

Connected through hyperedge HE-1 (induced by source paper "encore ensemble learning using convolution neural machine translation for automatic program repair"):
  with members:
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "code2vec learning distributed representations of code"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "fixminer mining relevant fix patterns for automated program repair"
    - "tree2tree neural translation model for learning source code changes"

Connected through hyperedge HE-2 (induced by source paper "tree transformer a transformer based method for correction of tree structured data"):
  with members:
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "automatic software repair a bibliography"
    - "tree2tree neural translation model for learning source code changes"
    - "learning to repair software vulnerabilities with generative adversarial networks"
    - "tree to tree neural networks for program translation"
    - "a nested attention neural hybrid model for grammatical error correction"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "spoc search based pseudocode to code"):
  with members:
    - "neural program repair by jointly learning to localize and repair"
    - "frangel component based synthesis with control structures"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "naps natural program synthesis dataset"
    - "mapping language to code in programmatic context"
    - "coarse to fine decoding for neural semantic parsing"
    - "retrieval based neural code generation"

Connected through hyperedge HE-4 (induced by source paper "a literature study of embeddings on source code"):
  with members:
    - "import2vec learning embeddings for software libraries"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "semantic embeddings for program behavior patterns"
    - "code2vec learning distributed representations of code"
    - "the remarkable role of similarity in redundancy based program repair"
    - "learning to represent edits"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "path based function embedding and its application to specification mining"

Connected through hyperedge HE-5 (induced by source paper "evaluating semantic representations of source code"):
  with members:
    - "coset a benchmark for evaluating neural program embeddings"
    - "a literature study of embeddings on source code"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "path based function embedding and its application to specification mining"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "automated software vulnerability detection with machine learning"
    - "code2seq generating sequences from structured representations of code"

Connected through hyperedge HE-6 (induced by source paper "neural program repair by jointly learning to localize and repair"):
  with members:
    - "automatic software repair a bibliography"
    - "robustfill neural program learning under noisy i o"
    - "making neural programming architectures generalize via recursion"
    - "neural message passing for quantum chemistry"
    - "learning to represent programs with graphs"
    - "dynamic neural program embedding for program repair"
    - "learning to superoptimize programs"
    - "sk_p a neural program corrector for moocs"

Connected through hyperedge HE-7 (induced by source paper "getafix learning to fix bugs automatically"):
  with members:
    - "learning to represent edits"
    - "deepbugs a learning approach to name based bug detection"
    - "coming a tool for mining change pattern instances from git commits"
    - "learning quick fixes from code repositories"
    - "a survey of machine learning for big code and naturalness"
    - "learning syntactic program transformations from examples"
    - "mining software repair models for reasoning on the search space of automated program fixing"
    - "npefix automatic runtime repair of null pointer exceptions in java"

Connected through hyperedge HE-8 (induced by source paper "learning to repair software vulnerabilities with generative adversarial networks"):
  with members:
    - "generative adversarial networks an overview"
    - "automated software vulnerability detection with machine learning"
    - "neural language correction with character based attention"
    - "learning from simulated and unsupervised images through adversarial training"
    - "conditional generative adversarial nets"

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.AI(Artificial Intelligence)