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: "aroma code recommendation via structural code search"
  Abstract: Programmers often write code that has similarity to existing code written somewhere. A tool that could help programmers to search such similar code would be immensely useful. Such a tool could help programmers to extend partially written code snippets to completely implement necessary functionality, help to discover extensions to the partial code which are commonly included by other programmers, help to cross-check against similar code written by other programmers, or help to add extra code which would fix common mistakes and errors. We propose Aroma, a tool and technique for code recommendation via structural code search. Aroma indexes a huge code corpus including thousands of open-source projects, takes a partial code snippet as input, searches the corpus for method bodies containing the partial code snippet, and clusters and intersects the results of the search to recommend a small set of succinct code snippets which both contain the query snippet and appear as part of several methods in the corpus. We evaluated Aroma on 2000 randomly selected queries created from the corpus, as well as 64 queries derived from code snippets obtained from Stack Overflow, a popular website for discussing code. We implemented Aroma for 4 different languages, and developed an IDE plugin for Aroma. Furthermore, we conducted a study where we asked 12 programmers to complete programming tasks using Aroma, and collected their feedback. Our results indicate that Aroma is capable of retrieving and recommending relevant code snippets efficiently.

Connected through hyperedge HE-1 (induced by source paper "trans 3 a transformer based framework for unifying code summarization and code search"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "ernie enhanced representation through knowledge integration"
    - "a neural model for generating natural language summaries of program subroutines"
    - "characterizing and detecting cuda program bugs"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deepbillboard systematic physical world testing of autonomous driving systems"
    - "deep reinforcement learning for sequence to sequence models"
    - "active inductive logic programming for code search"

Connected through hyperedge HE-2 (induced by source paper "boosting api recommendation with implicit feedback"):
  with members:
    - "recommending comprehensive solutions for programming tasks by mining crowd knowledge"
    - "effective reformulation of query for code search using crowdsourced knowledge and extra large data analytics"
    - "bridging semantic gaps between natural languages and apis with word embedding"
    - "webapirec recommending web apis to software projects via personalized ranking"
    - "xgboost a scalable tree boosting system"
    - "parameter free probabilistic api mining across github"
    - "rack automatic api recommendation using crowdsourced knowledge"
    - "quickar automatic query reformulation for concept location using crowdsourced knowledge"

Connected through hyperedge HE-3 (induced by source paper "when deep learning met code search"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "code2vec learning distributed representations of code"
    - "interpretable lstms for whole brain neuroimaging analyses"
    - "analyzing neuroimaging data through recurrent deep learning models"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "robust text to sql generation with execution guided decoding"
    - "a deep tree based model for software defect prediction"
    - "semantically enhanced software traceability using deep learning techniques"

Connected through hyperedge HE-4 (induced by source paper "neural code search evaluation dataset"):
  with members:
    - "when deep learning met code search"

Connected through hyperedge HE-5 (induced by source paper "a zero positive learning approach for diagnosing software performance regressions"):
  with members:
    - "greenhouse a zero positive machine learning system for time series anomaly detection"
    - "interpreting neural network judgments via minimal stable and symbolic corrections"
    - "the three pillars of machine programming"
    - "what regularized auto encoders learn from the data generating distribution"

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)