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: "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
  Abstract: We present an algorithm for simultaneous face detection, landmarks localization, pose estimation and gender recognition using deep convolutional neural networks (CNN). The proposed method called, HyperFace, fuses the intermediate layers of a deep CNN using a separate CNN followed by a multi-task learning algorithm that operates on the fused features. It exploits the synergy among the tasks which boosts up their individual performances. Additionally, we propose two variants of HyperFace: (1) HyperFace-ResNet that builds on the ResNet-101 model and achieves significant improvement in performance, and (2) Fast-HyperFace that uses a high recall fast face detector for generating region proposals to improve the speed of the algorithm. Extensive experiments show that the proposed models are able to capture both global and local information in faces and performs significantly better than many competitive algorithms for each of these four tasks.

Connected through hyperedge HE-1 (induced by source paper "two stream aural visual affect analysis in the wild"):
  with members:
    - "multi label class balancing algorithm for action unit detection"
    - "analysing affective behavior in the first abaw 2020 competition"
    - "fau facial expressions valence and arousal a multi task solution"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "context aware emotion recognition networks"
    - "retinaface single stage dense face localisation in the wild"
    - "face behavior a la carte expressions affect and action units in a single network"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-2 (induced by source paper "learning preference based similarities from face images using siamese multi task cnns"):
  with members:
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "smoothgrad removing noise by adding noise"
    - "an overview of multi task learning in deep neural networks"
    - "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"
    - "faceboxes a cpu real time face detector with high accuracy"
    - "cross stitch networks for multi task learning"
    - "densely connected convolutional networks"
    - "some like it hot visual guidance for preference prediction"

Connected through hyperedge HE-3 (induced by source paper "cross modal multi task learning for graphic recognition of caricature face"):
  with members:
    - "multi task convolutional neural network for pose invariant face recognition"
    - "deep cross modal learning for caricature verification and identification cavinet"
    - "sphereface deep hypersphere embedding for face recognition"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "an overview of multi task learning in deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "jaa net joint facial action unit detection and face alignment via adaptive attention"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "deep structure inference network for facial action unit recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "facial action unit detection using attention and relation learning"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "hand keypoint detection in single images using multiview bootstrapping"

Connected through hyperedge HE-5 (induced by source paper "meta learning extractors for music source separation"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "audio query based music source separation"
    - "on the variance of the adaptive learning rate and beyond"
    - "recursive speech separation for unknown number of speakers"
    - "lookahead optimizer k steps forward 1 step back"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "meta learning a dynamical language model"
    - "wave u net a multi scale neural network for end to end audio source separation"

Connected through hyperedge HE-6 (induced by source paper "volumetric landmark detection with a multi scale shift equivariant neural network"):
  with members:
    - "making convolutional networks shift invariant again"
    - "unsupervised learning of probabilistic diffeomorphic registration for images and surfaces"
    - "cnn based landmark detection in cardiac cta scans"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-7 (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-8 (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"

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)