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: "when deep learning met code search"
  Abstract: There have been multiple recent proposals on using deep neural networks for code search using natural language. Common across these proposals is the idea of $\mathit{embedding}$ code and natural language queries, into real vectors and then using vector distance to approximate semantic correlation between code and the query. Multiple approaches exist for learning these embeddings, including $\mathit{unsupervised}$ techniques, which rely only on a corpus of code examples, and $\mathit{supervised}$ techniques, which use an $\mathit{aligned}$ corpus of paired code and natural language descriptions. The goal of this supervision is to produce embeddings that are more similar for a query and the corresponding desired code snippet. #R##N#Clearly, there are choices in whether to use supervised techniques at all, and if one does, what sort of network and training to use for supervision. This paper is the first to evaluate these choices systematically. To this end, we assembled implementations of state-of-the-art techniques to run on a common platform, training and evaluation corpora. To explore the design space in network complexity, we also introduced a new design point that is a $\mathit{minimal}$ supervision extension to an existing unsupervised technique. #R##N#Our evaluation shows that: 1. adding supervision to an existing unsupervised technique can improve performance, though not necessarily by much; 2. simple networks for supervision can be more effective that more sophisticated sequence-based networks for code search; 3. while it is common to use docstrings to carry out supervision, there is a sizeable gap between the effectiveness of docstrings and a more query-appropriate supervision corpus.

Connected through hyperedge HE-1 (induced by source paper "searching a database of source codes using contextualized code search"):
  with members:
    - "multi modal attention network learning for semantic source code retrieval"
    - "on learning meaningful code changes via neural machine translation"
    - "mapping language to code in programmatic context"
    - "tree2tree neural translation model for learning source code changes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "top down tree structured decoding with syntactic connections for neural machine translation and parsing"
    - "robustfill neural program learning under noisy i o"
    - "variational deep semantic hashing for text documents"

Connected through hyperedge HE-2 (induced by source paper "adversarial examples for models of code"):
  with members:
    - "combating adversarial misspellings with robust word recognition"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "getafix learning to fix bugs automatically"
    - "structural language models for any code generation"
    - "code2vec learning distributed representations of code"
    - "targeted adversarial examples for black box audio systems"
    - "a general path based representation for predicting program properties"
    - "did you hear that adversarial examples against automatic speech recognition"

Connected through hyperedge HE-3 (induced by source paper "software engineering meets deep learning a literature review"):
  with members:
    - "feature maps a comprehensible software representation for design pattern detection"
    - "on learning meaningful code changes via neural machine translation"
    - "on using machine learning to identify knowledge in api reference documentation"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "a comprehensive study on deep learning bug characteristics"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "csindexbr exploring the brazilian scientific production in computer science"

Connected through hyperedge HE-4 (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"
    - "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"
    - "the adverse effects of code duplication in machine learning models of code"

Connected through hyperedge HE-5 (induced by source paper "codesearchnet challenge evaluating the state of semantic code search"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "structured neural summarization"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "neural code search evaluation dataset"):
  with members:
    - "aroma code recommendation via structural code search"

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)