human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deeptriage exploring the effectiveness of deep learning for bug triaging"):
  Abstract: For a given software bug report, identifying an appropriate developer who could potentially fix the bug is the primary task of a bug triaging process. A bug title (summary) and a detailed description is present in most of the bug tracking systems. Automatic bug triaging algorithm can be formulated as a classification problem, with the bug title and description as the input, mapping it to one of the available developers (classes). The major challenge is that the bug description usually contains a combination of free unstructured text, code snippets, and stack trace making the input data noisy. The existing bag-of-words (BOW) feature models do not consider the syntactical and sequential word information available in the unstructured text. We propose a novel bug report representation algorithm using an attention based deep bidirectional recurrent neural network (DBRNN-A) model that learns a syntactic and semantic feature from long word sequences in an unsupervised manner. Instead of BOW features, the DBRNN-A based bug representation is then used for training the classifier. Using an attention mechanism enables the model to learn the context representation over a long word sequence, as in a bug report. To provide a large amount of data to learn the feature learning model, the unfixed bug reports (~70% bugs in an open source bug tracking system) are leveraged, which were completely ignored in the previous studies. Another contribution is to make this research reproducible by making the source code available and creating a public benchmark dataset of bug reports from three open source bug tracking system: Google Chromium (383,104 bug reports), Mozilla Core (314,388 bug reports), and Mozilla Firefox (162,307 bug reports). Experimentally we compare our approach with BOW model and machine learning approaches and observe that DBRNN-A provides a higher rank-10 average accuracy.

Includes members:
  Member "developer prioritization in bug repositories":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a systematic mapping study of developer social network research")
  Member "effective approaches to attention based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention over parameters for dialogue systems")
  Member "distributed representations of sentences and documents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "learning phrase representations using rnn encoder decoder for statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "dropout improves recurrent neural networks for handwriting recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving long handwritten text line recognition with convolutional multi way associative memory")
  Member "speech recognition with deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

Question: Please tell me which class the source paper 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)