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: "deceiving google s cloud video intelligence api built for summarizing videos"
  Abstract: Despite the rapid progress of the techniques for image classification, video annotation has remained a challenging task. Automated video annotation would be a breakthrough technology, enabling users to search within the videos. Recently, Google introduced the Cloud Video Intelligence API for video analysis. As per the website, the system can be used to "separate signal from noise, by retrieving relevant information at the video, shot or per frame" level. A demonstration website has been also launched, which allows anyone to select a video for annotation. The API then detects the video labels (objects within the video) as well as shot labels (description of the video events over time). In this paper, we examine the usability of the Google's Cloud Video Intelligence API in adversarial environments. In particular, we investigate whether an adversary can subtly manipulate a video in such a way that the API will return only the adversary-desired labels. For this, we select an image, which is different from the video content, and insert it, periodically and at a very low rate, into the video. We found that if we insert one image every two seconds, the API is deceived into annotating the video as if it only contained the inserted image. Note that the modification to the video is hardly noticeable as, for instance, for a typical frame rate of 25, we insert only one image per 50 video frames. We also found that, by inserting one image per second, all the shot labels returned by the API are related to the inserted image. We perform the experiments on the sample videos provided by the API demonstration website and show that our attack is successful with different videos and images.

Connected through hyperedge HE-1 (induced by source paper "undersensitivity in neural reading comprehension"):
  with members:
    - "learning to ask unanswerable questions for machine reading comprehension"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "roberta a robustly optimized bert pretraining approach"
    - "robust machine comprehension models via adversarial training"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "what makes reading comprehension questions easier"

Connected through hyperedge HE-2 (induced by source paper "make up your mind adversarial generation of inconsistent natural language explanations"):
  with members:
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "annotation artifacts in natural language inference data"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "textual explanations for self driving vehicles"
    - "e snli natural language inference with natural language explanations"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-3 (induced by source paper "adversarially regularising neural nli models to integrate logical background knowledge"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "generating sentences by editing prototypes"
    - "ask the right questions active question reformulation with reinforcement learning"
    - "synthetic and natural noise both break neural machine translation"
    - "adversarial sets for regularising neural link predictors"
    - "adversarial examples for evaluating reading comprehension systems"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-4 (induced by source paper "enhancing robustness of machine learning systems via data transformations"):
  with members:
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "early methods for detecting adversarial images"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "a study of the effect of jpg compression on adversarial images"
    - "spoofing 2d face detection machines see people who aren t there"
    - "foveation based mechanisms alleviate adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "google s cloud vision api is not robust to noise"):
  with members:
    - "deceiving google s perspective api built for detecting toxic comments"
    - "blocking transferability of adversarial examples in black box learning systems"
    - "quality resilient deep neural networks"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "examining the impact of blur on recognition by convolutional networks"
    - "practical black box attacks against machine learning"
    - "how image degradations affect deep cnn based face recognition"
    - "concrete problems in ai safety"

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)