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: "verification of binarized neural networks via inter neuron factoring"
  Abstract: We study the problem of formal verification of Binarized Neural Networks (BNN), which have recently been proposed as a energy-efficient alternative to traditional learning networks. The verification of BNNs, using the reduction to hardware verification, can be even more scalable by factoring computations among neurons within the same layer. By proving the NP-hardness of finding optimal factoring as well as the hardness of PTAS approximability, we design polynomial-time search heuristics to generate factoring solutions. The overall framework allows applying verification techniques to moderately-sized BNNs for embedded devices with thousands of neurons and inputs.

Connected through hyperedge HE-1 (induced by source paper "algorithms for verifying deep neural networks"):
  with members:
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "juliareach a toolbox for set based reachability"
    - "deep neural network compression for aircraft collision avoidance systems"
    - "automated verification of neural networks advances challenges and perspectives"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "reachable set estimation and safety verification for piecewise linear systems with neural network controllers"
    - "efficient neural network robustness certification with general activation functions"

Connected through hyperedge HE-2 (induced by source paper "fannet formal analysis of noise tolerance training bias and input sensitivity in neural networks"):
  with members:
    - "a survey on bias and fairness in machine learning"
    - "formal verification of cnn based perception systems"
    - "verifying fairness properties via concentration"
    - "efficient formal safety analysis of neural networks"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "formal verification of piece wise linear feed forward neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "a confidence based approach for balancing fairness and accuracy"

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)