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: "from facial parts responses to face detection a deep learning approach"
  Abstract: In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is 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 variation, which are the main difficulty and bottleneck of most existing face detection approaches. We show that despite the use of DCN, our network can achieve practical runtime speed.

Connected through hyperedge HE-1 (induced by source paper "kpnet towards minimal face detector"):
  with members:
    - "bottom up object detection by grouping extreme and center points"
    - "pfld a practical facial landmark detector"
    - "dsfd dual shot face detector"
    - "look at boundary a boundary aware face alignment algorithm"
    - "cornernet detecting objects as paired keypoints"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "gradient harmonized single stage detector"

Connected through hyperedge HE-2 (induced by source paper "emerging disentanglement in auto encoder based unsupervised image content transfer"):
  with members:
    - "a two step disentanglement method"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "dida disentangled synthesis for domain adaptation"
    - "image to image translation for cross domain disentanglement"
    - "drit diverse image to image translation via disentangled representations"
    - "infovae information maximizing variational autoencoders"
    - "emergence of invariance and disentanglement in deep representations"

Connected through hyperedge HE-3 (induced by source paper "poet bin power efficient tiny binary neurons"):
  with members:
    - "deep forest towards an alternative to deep neural networks"
    - "towards effective low bitwidth convolutional neural networks"
    - "deep learning on fpgas past present and future"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "text understanding with the attention sum reader network"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "deep learning for face recognition a critical analysis"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "face attention network an effective face detector for the occluded faces"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "the do s and don ts for cnn based face verification"

Connected through hyperedge HE-5 (induced by source paper "retinaface single stage dense face localisation in the wild"):
  with members:
    - "robust and high performance face detector"
    - "on learning 3d face morphable model from in the wild images"
    - "wider face and pedestrian challenge 2018 methods and results"
    - "improved selective refinement network for face detection"
    - "dense 3d face decoding over 2500fps joint texture shape convolutional mesh decoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"

Connected through hyperedge HE-6 (induced by source paper "refineface refinement neural network for high performance face detection"):
  with members:
    - "robust and high performance face detector"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "pyramidbox high performance detector for finding tiny face"
    - "retinaface single stage dense face localisation in the wild"
    - "accurate face detection for high performance"
    - "improved selective refinement network for face detection"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"

Connected through hyperedge HE-7 (induced by source paper "extd extremely tiny face detector via iterative filter reuse"):
  with members:
    - "depthwise convolution is all you need for learning multiple visual domains"
    - "thundernet towards real time generic object detection"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "improved selective refinement network for face detection"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "dsfd dual shot face detector"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-8 (induced by source paper "improved selective refinement network for face detection"):
  with members:
    - "robust and high performance face detector"
    - "rethinking imagenet pre training"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"
    - "group normalization"
    - "fa rpn floating region proposals for face detection"
    - "real time rotation invariant face detection with progressive calibration networks"
    - "pyramidbox a context assisted single shot face detector"

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)