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: "towards analyzing semantic robustness of deep neural networks"
  Abstract: Despite the impressive performance of Deep Neural Networks (DNNs) on various vision tasks, they still exhibit erroneous high sensitivity toward semantic primitives (e.g. object pose). We propose a theoretically grounded analysis for DNNs robustness in the semantic space. We qualitatively analyze different DNNs semantic robustness by visualizing the DNN global behavior as semantic maps and observe interesting behavior of some DNNs. Since generating these semantic maps does not scale well with the dimensionality of the semantic space, we develop a bottom-up approach to detect robust regions of DNNs. To achieve this, We formalize the problem of finding robust semantic regions of the network as optimization of integral bounds and develop expressions for update directions of the region bounds. We use our developed formulations to quantitatively evaluate the semantic robustness of different famous network architectures. We show through extensive experimentation that several networks, though trained on the same dataset and while enjoying comparable accuracy, they do not necessarily perform similarly in semantic robustness. For example, InceptionV3 is more accurate despite being less semantically robust than ResNet50. We hope that this tool will serve as the first milestone towards understanding the semantic robustness of DNNs.

Connected through hyperedge HE-1 (induced by source paper "towards verifying robustness of neural networks against semantic perturbations"):
  with members:
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "scaling provable adversarial defenses"
    - "semantic adversarial examples"
    - "a dual approach to scalable verification of deep networks"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "efficient formal safety analysis of neural networks"

Connected through hyperedge HE-2 (induced by source paper "sada semantic adversarial diagnostic attacks for autonomous applications"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "deep drone racing learning agile flight in dynamic environments"
    - "semantic adversarial examples"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "yolov3 an incremental improvement"
    - "generating 3d adversarial point clouds"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "sparse gaussian process temporal difference learning for marine robot navigation"

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)