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: "face alignment by local deep descriptor regression"
  Abstract: We present an algorithm for extracting key-point descriptors using deep convolutional neural networks (CNN). Unlike many existing deep CNNs, our model computes local features around a given point in an image. We also present a face alignment algorithm based on regression using these local descriptors. The proposed method called Local Deep Descriptor Regression (LDDR) is able to localize face landmarks of varying sizes, poses and occlusions with high accuracy. Deep Descriptors presented in this paper are able to uniquely and efficiently describe every pixel in the image and therefore can potentially replace traditional descriptors such as SIFT and HOG. Extensive evaluations on five publicly available unconstrained face alignment datasets show that our deep descriptor network is able to capture strong local features around a given landmark and performs significantly better than many competitive and state-of-the-art face alignment algorithms.

Connected through hyperedge HE-1 (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-2 (induced by source paper "gp gan gender preserving gan for synthesizing faces from landmarks"):
  with members:
    - "every smile is unique landmark guided diverse smile generation"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "densely connected pyramid dehazing network"
    - "began boundary equilibrium generative adversarial networks"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "pose guided person image generation"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "natural and effective obfuscation by head inpainting"

Connected through hyperedge HE-3 (induced by source paper "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"):
  with members:
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "face alignment robust to pose expressions and occlusions"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "pose invariant face alignment with a single cnn"
    - "deep heterogeneous feature fusion for template based face recognition"
    - "the do s and don ts for cnn based face verification"
    - "dense face alignment"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"

Connected through hyperedge HE-4 (induced by source paper "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"):
  with members:
    - "robust facial landmark detection under significant head poses and occlusion"
    - "dcnns on a diet sampling strategies for reducing the training set size"
    - "human pose estimation with iterative error feedback"
    - "pose invariant 3d face alignment"
    - "towards arbitrary view face alignment by recommendation trees"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "a convolution tree with deconvolution branches exploiting geometric relationships for single shot keypoint detection"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "structured feature learning for pose estimation"
    - "pose invariant 3d face alignment"
    - "caffe convolutional architecture for fast feature embedding"
    - "articulated pose estimation by a graphical model with image dependent pairwise relations"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "unconstrained still video based face verification with deep convolutional neural networks"):
  with members:
    - "deep heterogeneous feature fusion for template based face recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"
    - "neural aggregation network for video face recognition"
    - "template adaptation for face verification and identification"
    - "do we really need to collect millions of faces for effective face recognition"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "a fast and accurate unconstrained face detector"

Connected through hyperedge HE-7 (induced by source paper "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "ssd single shot multibox detector"
    - "a fast and accurate unconstrained face detector"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "finding tiny faces"
    - "locnet improving localization accuracy for object detection"
    - "multi view face detection using deep convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "an all in one convolutional neural network for face analysis"):
  with members:
    - "neural aggregation network for video face recognition"
    - "do we really need to collect millions of faces for effective face recognition"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "a fast and accurate unconstrained face detector"
    - "unconstrained face verification using deep cnn features"
    - "facenet a unified embedding for face recognition and clustering"
    - "a deep pyramid deformable part model for face detection"
    - "from facial parts responses to face detection a deep learning approach"

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)