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 "identifying model weakness with adversarial examiner"):
  Abstract: Machine learning models are usually evaluated according to the average case performance on the test set. However, this is not always ideal, because in some sensitive domains (e.g. autonomous driving), it is the worst case performance that matters more. In this paper, we are interested in systematic exploration of the input data space to identify the weakness of the model to be evaluated. We propose to use an adversarial examiner in the testing stage. Different from the existing strategy to always give the same (distribution of) test data, the adversarial examiner will dynamically select the next test data to hand out based on the testing history so far, with the goal being to undermine the model's performance. This sequence of test data not only helps us understand the current model, but also serves as constructive feedback to help improve the model in the next iteration. We conduct experiments on ShapeNet object classification. We show that our adversarial examiner can successfully put more emphasis on the weakness of the model, preventing performance estimates from being overly optimistic.

Includes members:
  Member "using videos to evaluate image model robustness":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")
  Member "embodied visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an exploration of embodied visual exploration")
  Member "meta sim learning to generate synthetic datasets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stylegan2 distillation for feed forward image manipulation")
  Member "gqa a new dataset for real world visual reasoning and compositional question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unbiased scene graph generation from biased training")
  Member "multi granularity hierarchical attention fusion networks for reading comprehension and question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "realistic adversarial examples in 3d meshes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "deep nets what have they ever done for vision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "synthetic humans for action recognition from unseen viewpoints")
  Member "strike with a pose neural networks are easily fooled by strange poses of familiar objects":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")

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)