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 to encode and classify test executions"):
  Abstract: The challenge of automatically determining the correctness of test executions is referred to as the test oracle problem and is one of the key remaining issues for automated testing. The goal in this paper is to solve the test oracle problem in a way that is general, scalable and accurate. #R##N#To achieve this, we use supervised learning over test execution traces. We label a small fraction of the execution traces with their verdict of pass or fail. We use the labelled traces to train a neural network (NN) model to learn to distinguish runtime patterns for passing versus failing executions for a given program. Our approach for building this NN model involves the following steps, 1. Instrument the program to record execution traces as sequences of method invocations and global state, 2. Label a small fraction of the execution traces with their verdicts, 3. Designing a NN component that embeds information in execution traces to fixed length vectors, 4. Design a NN model that uses the trace information for classification, 5. Evaluate the inferred classification model on unseen execution traces from the program. #R##N#We evaluate our approach using case studies from different application domains: 1. Module from Ethereum Blockchain, 2. Module from PyTorch deep learning framework, 3. Microsoft SEAL encryption library components, 4. Sed stream editor, 5. Value pointer library and 6. Nine network protocols from Linux packet identifier, L7-Filter. We found the classification models for all subject programs resulted in high precision, recall and specificity, over 95%, while only training with an average 9% of the total traces. Our experiments show that the proposed neural network model is highly effective as a test oracle and is able to learn runtime patterns to distinguish passing and failing test executions for systems and tests from different application domains.

Includes members:
  Member "code2vec learning distributed representations of code":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial robustness for code")
  Member "code2seq generating sequences from structured representations of code":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "learning to represent programs with graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial robustness for code")
  Member "dynamic neural program embedding for program repair":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cc2vec distributed representations of code changes")
  Member "a convolutional attention network for extreme summarization of source code":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)