human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "sympas symbolic program slicing"
  Abstract: Program slicing is a technique for simplifying programs by focusing on selected aspects of their behaviour. Current mainstream static slicing methods operate on the PDG (program dependence graph) or SDG (system dependence graph), but these friendly graph representations may be expensive and error-prone for some users. We attempt in this paper to study a light-weight approach of static program slicing, called Symbolic Program Slicing (SymPas), which works as a dataflow analysis on LLVM (Low-Level Virtual Machine). In our SymPas approach, slices are stored symbolically rather than procedure being re-analysed (cf. procedure summaries). Instead of re-analysing a procedure multiple times to find its slices for each callling context, SymPas calculates a single symbolic (or parameterized) slice which can be instantiated at call sites avoiding re-analysis; it is implemented in LLVM to perform slicing on its intermediate representation (IR). For comparison, we systematically adapt IFDS (Interprocedural Finite Distributive Subset) analysis and the SDG-based slicing method (SDG-IFDS) to statically IR slice programs. Evaluated on open-source and benchmark programs, our backward SymPas shows a factor-of-6 reduction in time cost and a factor-of-4 reduction in space cost, compared to backward SDG-IFDS, thus being more efficient. In addition, the result shows that after studying slices from 66 programs, ranging up to 336,800 IR instructions in size, SymPas is highly size-scalable.

Connected through hyperedge HE-1 (induced by source paper "saturn software deobfuscation framework based on llvm"):
  with members:
    - "how to kill symbolic deobfuscation for free or unleashing the potential of path oriented protections"
    - "defeating opaque predicates statically through machine learning and binary analysis"
    - "souper a synthesizing superoptimizer"

Question: Please tell me which class the center node 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.PL(Programming Languages)