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: "faceness net face detection through deep facial part responses"
  Abstract: We propose a deep convolutional neural network (CNN) for face detection leveraging on facial attributes based supervision. We observe a phenomenon that part detectors emerge within CNN trained to classify attributes from uncropped face images, without any explicit part supervision. The observation motivates a new method for finding faces through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is data-driven, and carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variations. Our method achieves promising performance on popular benchmarks including FDDB, PASCAL Faces, AFW, and WIDER FACE.

Connected through hyperedge HE-1 (induced by source paper "deephmap combined projection grouping and correspondence learning for full dof pose estimation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "real time object pose estimation with pose interpreter networks"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "a unified framework for multi view multi class object pose estimation"
    - "physics based scene level reasoning for object pose estimation in clutter"

Connected through hyperedge HE-2 (induced by source paper "detector in detector multi level analysis for human parts"):
  with members:
    - "face magnet magnifying feature maps to detect small faces"
    - "aggregated channels network for real time pedestrian detection"
    - "fssd feature fusion single shot multibox detector"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "a jointly learned deep architecture for facial attribute analysis and face detection in the wild"
    - "citypersons a diverse dataset for pedestrian detection"
    - "towards accurate multi person pose estimation in the wild"

Connected through hyperedge HE-3 (induced by source paper "anchor cascade for efficient face detection"):
  with members:
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "face detection through scale friendly deep convolutional networks"
    - "scale aware face detection"
    - "recurrent scale approximation for object detection in cnn"
    - "ssh single stage headless face detector"
    - "face detection with end to end integration of a convnet and a 3d model"
    - "a unified multi scale deep convolutional neural network for fast object detection"

Connected through hyperedge HE-4 (induced by source paper "event based face detection and tracking in the blink of an eye"):
  with members:
    - "dart distribution aware retinal transform for event based cameras"
    - "face detection using deep learning an improved faster rcnn approach"
    - "face detection with the faster r cnn"
    - "ssd single shot multibox detector"
    - "wider face a face detection benchmark"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "face from depth for head pose estimation on depth images"):
  with members:
    - "improving heterogeneous face recognition with conditional adversarial networks"
    - "generative adversarial models for people attribute recognition in surveillance"
    - "poseidon face from depth for driver pose estimation"
    - "generative image modeling using style and structure adversarial networks"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "coupled generative adversarial networks"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-6 (induced by source paper "face detection through scale friendly deep convolutional networks"):
  with members:
    - "to boost or not to boost on the limits of boosted trees for object detection"
    - "face detection with end to end integration of a convnet and a 3d model"
    - "face detection with the faster r cnn"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "ssd single shot multibox detector"
    - "grid loss detecting occluded faces"
    - "finding tiny faces"
    - "deep residual learning for image recognition"

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)