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 "core automating review recommendation for code changes"):
  Abstract: Code review is a common process that is used by developers, in which a reviewer provides useful comments or points out defects in the submitted source code changes via pull request. Code review has been widely used for both industry and open-source projects due to its capacity in early defect identification, project maintenance, and code improvement. With rapid updates on project developments, code review becomes a non-trivial and labor-intensive task for reviewers. Thus, an automated code review engine can be beneficial and useful for project development in practice. Although there exist prior studies on automating the code review process by adopting static analysis tools or deep learning techniques, they often require external sources such as partial or full source code for accurate review suggestion. In this paper, we aim at automating the code review process only based on code changes and the corresponding reviews but with better performance. The hinge of accurate code review suggestion is to learn good representations for both code changes and reviews. To achieve this with limited source, we design a multi-level embedding (i.e., word embedding and character embedding) approach to represent the semantics provided by code changes and reviews. The embeddings are then well trained through a proposed attentional deep learning model, as a whole named CORE. We evaluate the effectiveness of CORE on code changes and reviews collected from 19 popular Java projects hosted on Github. Experimental results show that our model CORE can achieve significantly better performance than the state-of-the-art model (DeepMem), with an increase of 131.03% in terms of Recall@10 and 150.69% in terms of Mean Reciprocal Rank. Qualitative general word analysis among project developers also demonstrates the performance of CORE in automating code review.

Includes members:
  Member "an empirical assessment of security risks of global android banking apps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quality assessment of online automated privacy policy generators an empirical study")
  Member "pythia ai assisted code completion system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning autocomplete systems as a communication game")
  Member "an empirical study towards characterizing deep learning development and deployment across different frameworks and platforms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "software engineering practice in the development of deep learning applications")
  Member "storydroid automated generation of storyboard for android apps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unblind your apps predicting natural language labels for mobile gui components by deep learning")
  Member "ausera large scale automated security risk assessment of global mobile banking apps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an empirical assessment of security risks of global android banking apps")
  Member "efficiently manifesting asynchronous programming errors in android apps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an empirical assessment of security risks of global android banking apps")
  Member "code completion with neural attention and pointer networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "deep contextualized word representations":
    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)