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 "inputs from hell generating uncommon inputs from common samples"):
  Abstract: Generating structured input files to test programs can be performed by techniques that produce them from a grammar that serves as the specification for syntactically correct input files. Two interesting scenarios then arise for effective testing. In the first scenario, software engineers would like to generate inputs that are as similar as possible to the inputs in common usage of the program, to test the reliability of the program. More interesting is the second scenario where inputs should be as dissimilar as possible from normal usage. This is useful for robustness testing and exploring yet uncovered behavior. To provide test cases for both scenarios, we leverage a context-free grammar to parse a set of sample input files that represent the program's common usage, and determine probabilities for individual grammar production as they occur during parsing the inputs. Replicating these probabilities during grammar-based test input generation, we obtain inputs that are close to the samples. Inverting these probabilities yields inputs that are strongly dissimilar to common inputs, yet still valid with respect to the grammar. Our evaluation on three common input formats (JSON, JavaScript, CSS) shows the effectiveness of these approaches in obtaining instances from both sets of inputs.

Includes members:
  Member "learn fuzz machine learning for input fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "synthesizing program input grammars":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a systematic review of fuzzing based on machine learning techniques")

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)