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: "saliency benchmarking separating models maps and metrics"
  Abstract: The field of fixation prediction is heavily model-driven, with dozens of new models published every year. However, progress in the field can be difficult to judge because models are compared using a variety of inconsistent metrics. As soon as a saliency map is optimized for a certain metric, it is penalized by other metrics. Here we propose a principled approach to solve the benchmarking problem: we separate the notions of saliency models and saliency maps. We define a saliency model to be a probabilistic model of fixation density prediction and, inspired by Bayesian decision theory, a saliency map to be a metric-specific prediction derived from the model density which maximizes the expected performance on that metric. We derive the optimal saliency map for the most commonly used saliency metrics (AUC, sAUC, NSS, CC, SIM, KL-Div) and show that they can be computed analytically or approximated with high precision using the model density. We show that this leads to consistent rankings in all metrics and avoids the penalties of using one saliency map for all metrics. Under this framework, "good" models will perform well in all metrics.

Connected through hyperedge HE-1 (induced by source paper "adversarial vision challenge"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial logit pairing"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "deep mutual learning"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"

Connected through hyperedge HE-2 (induced by source paper "learning visual importance for graphic designs and data visualizations"):
  with members:
    - "bubbleview an interface for crowdsourcing image importance maps and tracking visual attention"
    - "a deep multi level network for saliency prediction"
    - "eye tracking for everyone"
    - "what do different evaluation metrics tell us about saliency models"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "turkergaze crowdsourcing saliency with webcam based eye tracking"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

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)