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: "toward guaranteed illumination models for non convex objects"
  Abstract: Illumination variation remains a central challenge in object detection and recognition. Existing analyses of illumination variation typically pertain to convex, Lambertian objects, and guarantee quality of approximation in an average case sense. We show that it is possible to build V(vertex)-description convex cone models with worst-case performance guarantees, for non-convex Lambertian objects. Namely, a natural verification test based on the angle to the constructed cone guarantees to accept any image which is sufficiently well-approximated by an image of the object under some admissible lighting condition, and guarantees to reject any image that does not have a sufficiently good approximation. The cone models are generated by sampling point illuminations with sufficient density, which follows from a new perturbation bound for point images in the Lambertian model. As the number of point images required for guaranteed verification may be large, we introduce a new formulation for cone preserving dimensionality reduction, which leverages tools from sparse and low-rank decomposition to reduce the complexity, while controlling the approximation error with respect to the original cone.

Connected through hyperedge HE-1 (induced by source paper "deepxplore automated whitebox testing of deep learning systems"):
  with members:
    - "formal security analysis of neural networks using symbolic intervals"
    - "learning to generate reviews and discovering sentiment"
    - "efficient formal safety analysis of neural networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "extending defensive distillation"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-2 (induced by source paper "towards practical verification of machine learning the case of computer vision systems"):
  with members:
    - "sok security and privacy in machine learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "practical black box attacks against machine learning"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-3 (induced by source paper "sparse representation based open set recognition"):
  with members:
    - "sequential score adaptation with extreme value theory for robust railway track inspection"

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.CV(Computer Vision and Pattern Recognition)