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 "learning blended precise semantic program embeddings"):
  Abstract: Learning neural program embeddings is key to utilizing deep neural networks in program languages research --- precise and efficient program representations enable the application of deep models to a wide range of program analysis tasks. Existing approaches predominately learn to embed programs from their source code, and, as a result, they do not capture deep, precise program semantics. On the other hand, models learned from runtime information critically depend on the quality of program executions, thus leading to trained models with highly variant quality. This paper tackles these inherent weaknesses of prior approaches by introducing a new deep neural network, \liger, which learns program representations from a mixture of symbolic and concrete execution traces. We have evaluated \liger on \coset, a recently proposed benchmark suite for evaluating neural program embeddings. Results show \liger (1) is significantly more accurate than the state-of-the-art syntax-based models Gated Graph Neural Network and code2vec in classifying program semantics, and (2) requires on average 10x fewer executions covering 74\% fewer paths than the state-of-the-art dynamic model \dypro. Furthermore, we extend \liger to predict the name for a method from its body's vector representation. Learning on the same set of functions (more than 170K in total), \liger significantly outperforms code2seq, the previous state-of-the-art for method name prediction.

Includes members:
  Member "coset a benchmark for evaluating neural program embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic robustness of models of source code")
  Member "learning scalable and precise representation of program semantics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning a static bug finder from data")
  Member "code2vec learning distributed representations of code":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial robustness for code")
  Member "code vectors understanding programs through embedded abstracted symbolic traces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cc2vec distributed representations of code changes")
  Member "code2seq generating sequences from structured representations of code":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "learning to represent programs with graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial robustness for code")
  Member "dynamic neural program embedding for program repair":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cc2vec distributed representations of code changes")

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)