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: "pushing the limits of unconstrained face detection a challenge dataset and baseline results"
  Abstract: Face detection has witnessed immense progress in the last few years, with new milestones being surpassed every year. While many challenges such as large variations in scale, pose, appearance are successfully addressed, there still exist several issues which are not specifically captured by existing methods or datasets. In this work, we identify the next set of challenges that requires attention from the research community and collect a new dataset of face images that involve these issues such as weather-based degradations, motion blur, focus blur and several others. We demonstrate that there is a considerable gap in the performance of state-of-the-art detectors and real-world requirements. Hence, in an attempt to fuel further research in unconstrained face detection, we present a new annotated Unconstrained Face Detection Dataset (UFDD) with several challenges and benchmark recent methods. Additionally, we provide an in-depth analysis of the results and failure cases of these methods. The dataset as well as baseline results will be made publicly available in due time. The UFDD dataset as well as baseline results are available at: www.ufdd.info/

Connected through hyperedge HE-1 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "enlightengan deep light enhancement without paired supervision"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "joint enhancement and denoising method via sequential decomposition"
    - "getting to know low light images with the exclusively dark dataset"
    - "yolov3 an incremental improvement"
    - "pyramidbox a context assisted single shot face detector"

Connected through hyperedge HE-2 (induced by source paper "dafe fd density aware feature enrichment for face detection"):
  with members:
    - "face magnet magnifying feature maps to detect small faces"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "single shot text detector with regional attention"

Connected through hyperedge HE-3 (induced by source paper "prior based domain adaptive object detection for adverse weather conditions"):
  with members:
    - "exploring object relation in mean teacher for cross domain detection"
    - "a robust learning approach to domain adaptive object detection"
    - "single image deraining a comprehensive benchmark analysis"
    - "automatic adaptation of object detectors to new domains using self training"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "vision meets drones a challenge"
    - "semantic foggy scene understanding with synthetic data"
    - "domain adaptive faster r cnn for object detection in the wild"

Connected through hyperedge HE-4 (induced by source paper "improving face detection performance with 3d rendered synthetic data"):
  with members:
    - "survey of face detection on low quality images"
    - "training deep face recognition systems with synthetic data"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "ganimation anatomically aware facial animation from a single image"
    - "sface an efficient network for face detection in large scale variations"
    - "recent progress of face image synthesis"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face 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)