human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "score cam improved visual explanations via score weighted class activation mapping"):
  Abstract: Recently, more and more attention has been drawn into the internal mechanism of the convolutional neural network and on what basis does the network make a specific decision. In this paper, we develop a novel post-hoc visual explanation method called Score-CAM based on class activation mapping. Unlike previous class activation mapping based approaches, Score-CAM gets rid of the dependence on gradient by obtaining the weight of each activation map through its forward passing score on target class, the final result is obtained by a linear combination of weights and activation maps. We demonstrate that Score-CAM achieves better visual performance with less noise and has better stability than Grad-CAM and Grad-CAM++. In the experiment, we rethink issues of previous evaluation metrics and propose a representative evaluation approach Energy- Based Pointing Game to measure the quality of the generated saliency maps. Our approach outperforms previous methods on energy-based pointing game and recognition and shows more robustness under adversarial attack.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "interpretable and fine grained visual explanations for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters")
  Member "smooth grad cam an enhanced inference level visualization technique for deep convolutional neural network models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interpreting interpretations organizing attribution methods by criteria")
  Member "u cam visual explanation using uncertainty based class activation maps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust explanations for visual question answering")
  Member "towards explanation of dnn based prediction with guided feature inversion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fda feature disruptive attack")
  Member "techniques for interpretable machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "local explanation methods for deep neural networks lack sensitivity to parameter values":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tedl a text encryption method based on deep learning")
  Member "explaining image classifiers by counterfactual generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters")

Question: Please tell me which class the source paper 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)