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: "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"
  Abstract: This paper aims to boost privacy-preserving visual recognition, an increasingly demanded feature in smart camera applications, using deep learning. We formulate a unique adversarial training framework, that learns a degradation transform for the original video inputs, in order to explicitly optimize the trade-off between target task performance and the associated privacy budgets on the degraded video. We carefully analyze and benchmark three different optimization strategies to train the resulting model. Notably, the privacy budget, often defined and measured in task-driven contexts, cannot be reliably indicated using any single model performance, because a strong protection of privacy has to sustain against any possible model that tries to hack privacy information. In order to tackle this problem, we propose two strategies: model restarting and model ensemble, which can be easily plug-and-play into our training algorithms and further improve the performance. Extensive experiments have been carried out and analyzed. On the other hand, few public datasets are available with both utility and privacy labels provided, making the power of data-driven (supervised) learning not yet fully unleashed on this task. We first discuss an innovative heuristic of cross-dataset training and evaluation, that jointly utilizes two datasets with target task and privacy labels respectively, for adversarial training. To further alleviate this challenge, we have constructed a new dataset, termed PA-HMDB51, with both target task (action) and selected privacy attributes (gender, age, race, nudity, and relationship) labeled on a frame-wise basis. This first-of-its-kind video dataset further validates the effectiveness of our proposed framework, and opens up new opportunities for the research community.

Connected through hyperedge HE-1 (induced by source paper "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "bridging the gap between computational photography and visual recognition"
    - "vision meets drones a challenge"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dada deep adversarial data augmentation for extremely low data regime classification"

Connected through hyperedge HE-2 (induced by source paper "deep poisoning functions towards robust privacy safe image data sharing"):
  with members:
    - "the deepfake detection challenge dfdc preview dataset"
    - "training with the invisibles obfuscating images to share safely for learning visual recognition models"
    - "deepobfuscator adversarial training framework for privacy preserving image classification"
    - "privacy preserving image based localization"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning to anonymize faces for privacy preserving action detection"
    - "a hybrid model for identity obfuscation by face replacement"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-3 (induced by source paper "calibrated domain invariant learning for highly generalizable large scale re identification"):
  with members:
    - "abd net attentive but diverse person re identification"
    - "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "ram a region aware deep model for vehicle re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "parameter free spatial attention network for person re identification"
    - "focal loss for dense object detection"

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)