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: "toward end to end face recognition through alignment learning"
  Abstract: A common practice in modern face recognition methods is to specifically align the face area based on the prior knowledge of human face structure before recognition feature extraction. The face alignment is usually implemented independently, causing difficulties in the designing of end-to-end face recognition models. We study the possibility of end-to-end face recognition through alignment learning in which neither prior knowledge on facial landmarks nor artificially defined geometric transformations are required. Only human identity clues are used for driving the automatic learning of appropriate geometric transformations for the face recognition task. Trained purely on publicly available datasets, our model achieves a verification accuracy of 99.33% on the LFW dataset, which is on par with state-of-the-art single model methods.

Connected through hyperedge HE-1 (induced by source paper "video person re identification by temporal residual learning"):
  with members:
    - "salient object detection by lossless feature reflection"
    - "person re identification by camera correlation aware feature augmentation"
    - "person re identification via recurrent feature aggregation"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "in defense of the triplet loss for person re identification"
    - "quality aware network for set to set recognition"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"

Connected through hyperedge HE-2 (induced by source paper "locality constrained spatial transformer network for video crowd counting"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"
    - "spatiotemporal modeling for crowd counting in videos"
    - "context aware trajectory prediction"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"

Connected through hyperedge HE-3 (induced by source paper "deep regionlets blended representation and deep learning for generic object detection"):
  with members:
    - "an analysis of pre training on object detection"
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "cornernet detecting objects as paired keypoints"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "deep feature pyramid reconfiguration for object detection"
    - "multi scale location aware kernel representation for object detection"
    - "pseudo mask augmented object detection"

Connected through hyperedge HE-4 (induced by source paper "gridface face rectification via learning local homography transformations"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "deep mean shift priors for image restoration"
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "quality aware network for set to set recognition"
    - "synthesizing normalized faces from facial identity features"
    - "towards large pose face frontalization in the wild"
    - "neural aggregation network for video face recognition"

Connected through hyperedge HE-5 (induced by source paper "statistical transformer networks learning shape and appearance models via self supervision"):
  with members:
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "convolutional neural network architecture for geometric matching"
    - "deformable convolutional networks"
    - "3d morphable models as spatial transformer networks"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "dense transformer networks"
    - "faceposenet making a case for landmark free face alignment"
    - "unsupervised learning of object landmarks by factorized spatial embeddings"

Connected through hyperedge HE-6 (induced by source paper "destnet densely fused spatial transformer networks"):
  with members:
    - "mask r cnn"
    - "harmonic networks deep translation and rotation equivariance"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "densely connected convolutional networks"
    - "image to image translation with conditional adversarial networks"
    - "inverse compositional spatial transformer networks"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "pose robust face recognition via deep residual equivariant mapping"):
  with members:
    - "multi task convolutional neural network for pose invariant face recognition"
    - "quality aware network for set to set recognition"
    - "do we really need to collect millions of faces for effective face recognition"
    - "pooling faces template based face recognition with pooled face images"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "face recognition using deep multi pose representations"
    - "triplet probabilistic embedding for face verification and clustering"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

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)