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: "how to manipulate cnns to make them lie the gradcam case"
  Abstract: Recently many methods have been introduced to explain CNN decisions. However, it has been shown that some methods can be sensitive to manipulation of the input. We continue this line of work and investigate the explanation method GradCAM. Instead of manipulating the input, we consider an adversary that manipulates the model itself to attack the explanation. By changing weights and architecture, we demonstrate that it is possible to generate any desired explanation, while leaving the model's accuracy essentially unchanged. This illustrates that GradCAM cannot explain the decision of every CNN and provides a proof of concept showing that it is possible to obfuscate the inner workings of a CNN. Finally, we combine input and model manipulation. To this end we put a backdoor in the network: the explanation is correct unless there is a specific pattern present in the input, which triggers a malicious explanation. Our work raises new security concerns, especially in settings where explanations of models may be used to make decisions, such as in the medical domain.

Connected through hyperedge HE-1 (induced by source paper "when explanations lie why many modified bp attributions fail"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "why are saliency maps noisy cause of and solution to noisy saliency maps"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "innvestigate neural networks"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "understanding individual decisions of cnns via contrastive backpropagation"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-2 (induced by source paper "deceptive ai explanations creation and detection"):
  with members:
    - "human grounded evaluations of explanation methods for text classification"
    - "a survey on bias and fairness in machine learning"
    - "how do i fool you manipulating user trust via misleading black box explanations"
    - "generating sentiment preserving fake online reviews using neural language models and their human and machine based detection"
    - "how model accuracy and explanation fidelity influence user trust"
    - "perturbation sensitivity analysis to detect unintended model biases"
    - "rise randomized input sampling for explanation of black box models"
    - "sanity checks for saliency maps"

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)