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: "a counterexample guided approach to finding numerical invariants"
  Abstract: Numerical invariants, e.g., relationships among numerical variables in a program, represent a useful class of properties to analyze programs. General polynomial invariants represent more complex numerical relations, but they are often required in many scientific and engineering applications. We present NumInv, a tool that implements a counterexample-guided invariant generation (CEGIR) technique to automatically discover numerical invariants, which are polynomial equality and inequality relations among numerical variables. This CEGIR technique infers candidate invariants from program traces and then checks them against the program source code using the KLEE test-input generation tool. If the invariants are incorrect KLEE returns counterexample traces, which help the dynamic inference obtain better results. Existing CEGIR approaches often require sound invariants, however NumInv sacrifices soundness and produces results that KLEE cannot refute within certain time bounds. This design and the use of KLEE as a verifier allow NumInv to discover useful and important numerical invariants for many challenging programs. #R##N#Preliminary results show that NumInv generates required invariants for understanding and verifying correctness of programs involving complex arithmetic. We also show that NumInv discovers polynomial invariants that capture precise complexity bounds of programs used to benchmark existing static complexity analysis techniques. Finally, we show that NumInv performs competitively comparing to state of the art numerical invariant analysis tools.

Connected through hyperedge HE-1 (induced by source paper "neuro symbolic execution the feasibility of an inductive approach to symbolic execution"):
  with members:
    - "angora efficient fuzzing by principled search"
    - "loopinvgen a loop invariant generator based on precondition inference"
    - "end to end learning for self driving cars"
    - "a continuum among logarithmic linear and exponential functions and its potential to improve generalization in neural networks"
    - "an overview of gradient descent optimization algorithms"
    - "tensorflow a system for large scale machine learning"
    - "counterexample guided polynomial loop invariant generation by lagrange interpolation"
    - "the limitations of deep learning in adversarial settings"

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.SE(Software Engineering)