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: "attacking optical flow"
  Abstract: Deep neural nets achieve state-of-the-art performance on the problem of optical flow estimation. Since optical flow is used in several safety-critical applications like self-driving cars, it is important to gain insights into the robustness of those techniques. Recently, it has been shown that adversarial attacks easily fool deep neural networks to misclassify objects. The robustness of optical flow networks to adversarial attacks, however, has not been studied so far. In this paper, we extend adversarial patch attacks to optical flow networks and show that such attacks can compromise their performance. We show that corrupting a small patch of less than 1% of the image size can significantly affect optical flow estimates. Our attacks lead to noisy flow estimates that extend significantly beyond the region of the attack, in many cases even completely erasing the motion of objects in the scene. While networks using an encoder-decoder architecture are very sensitive to these attacks, we found that networks using a spatial pyramid architecture are less affected. We analyse the success and failure of attacking both architectures by visualizing their feature maps and comparing them to classical optical flow techniques which are robust to these attacks. We also demonstrate that such attacks are practical by placing a printed pattern into real scenes.

Connected through hyperedge HE-1 (induced by source paper "analysis of explainers of black box deep neural networks for computer vision a survey"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"
    - "a general framework for adversarial examples with objectives"
    - "on the in fidelity and sensitivity for explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "visualbackprop efficient visualization of cnns"

Connected through hyperedge HE-2 (induced by source paper "using depth for pixel wise detection of adversarial attacks in crowd counting"):
  with members:
    - "detecting the unexpected via image resynthesis"
    - "bayesian loss for crowd count estimation with point supervision"
    - "multi level bottom top and top bottom feature fusion for crowd counting"
    - "point in box out beyond counting persons in crowds"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "learning spatial awareness to improve crowd counting"
    - "counting with focus for free"
    - "learn to scale generating multipolar normalized density map for crowd counting"

Connected through hyperedge HE-3 (induced by source paper "indirect local attacks for context aware semantic segmentation networks"):
  with members:
    - "detecting the unexpected via image resynthesis"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "dual attention network for scene segmentation"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "context encoding for semantic segmentation"
    - "dilated residual networks"

Connected through hyperedge HE-4 (induced by source paper "amora black box adversarial morphing attack"):
  with members:
    - "fakelocator robust localization of gan based face manipulations via semantic segmentation networks with bells and whistles"
    - "advfaces adversarial face synthesis"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "wasserstein smoothing certified robustness against wasserstein adversarial attacks"
    - "machine learning testing survey landscapes and horizons"
    - "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"

Connected through hyperedge HE-5 (induced by source paper "adversarial patches exploiting contextual reasoning in object detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "not using the car to see the sidewalk quantifying and controlling the effects of context in classification and segmentation"
    - "lavan localized and visible adversarial noise"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"

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)