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: "joint 3d face reconstruction and dense alignment with position map regression network"
  Abstract: We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment. To achieve this, we design a 2D representation called UV position map which records the 3D shape of a complete face in UV space, then train a simple Convolutional Neural Network to regress it from a single 2D image. We also integrate a weight mask into the loss function during training to improve the performance of the network. Our method does not rely on any prior face model, and can reconstruct full facial geometry along with semantic meaning. Meanwhile, our network is very light-weighted and spends only 9.8ms to process an image, which is extremely faster than previous works. Experiments on multiple challenging datasets show that our method surpasses other state-of-the-art methods on both reconstruction and alignment tasks by a large margin.

Connected through hyperedge HE-1 (induced by source paper "searching central difference convolutional networks for face anti spoofing"):
  with members:
    - "video action recognition via neural architecture searching"
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "deep pixel wise binary supervision for face presentation attack detection"
    - "deep tree learning for zero shot face anti spoofing"
    - "neural architecture search for deep face recognition"
    - "searching for a robust neural architecture in four gpu hours"
    - "customizable architecture search for semantic segmentation"
    - "auto reid searching for a part aware convnet for person re identification"

Connected through hyperedge HE-2 (induced by source paper "towards universal representation learning for deep face recognition"):
  with members:
    - "towards universal object detection by domain attention"
    - "probabilistic face embeddings"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for face recognition"
    - "additive margin softmax for face verification"
    - "on calibration of modern neural networks"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-3 (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-4 (induced by source paper "deep frequent spatial temporal learning for face anti spoofing"):
  with members:
    - "specular and diffuse reflection based face liveness detection for mobile devices"
    - "learning generalizable and identity discriminative representations for face anti spoofing"
    - "feathernets convolutional neural networks as light as feather for face anti spoofing"
    - "face de spoofing anti spoofing via noise modeling"
    - "discriminative representation combinations for accurate face spoofing detection"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "a dataset and benchmark for large scale multi modal face anti spoofing"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"

Connected through hyperedge HE-5 (induced by source paper "high accuracy face geometry capture using a smartphone video"):
  with members:
    - "3d face reconstruction with geometry details from a single image"
    - "detailed human avatars from monocular video"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "sparse photometric 3d face reconstruction guided by morphable models"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "robust registration and geometry estimation from unstructured facial scans"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-6 (induced by source paper "casia surf cefa a benchmark for multi modal cross ethnicity face anti spoofing"):
  with members:
    - "casia surf a large scale multi modal benchmark for face anti spoofing"
    - "face alignment in full pose range a 3d total solution"
    - "face de spoofing anti spoofing via noise modeling"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "a dataset and benchmark for large scale multi modal face anti spoofing"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "racial faces in the wild reducing racial bias by information maximization adaptation network"
    - "cooperative training of deep aggregation networks for rgb d action recognition"

Connected through hyperedge HE-7 (induced by source paper "deep spatial gradient and temporal depth learning for face anti spoofing"):
  with members:
    - "aurora guard real time face anti spoofing via light reflection"
    - "improving face anti spoofing by 3d virtual synthesis"
    - "exploring hypergraph representation on face anti spoofing beyond 2d attacks"
    - "discriminative representation combinations for accurate face spoofing detection"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "deep residual learning for image recognition"
    - "face anti spoofing based on color texture analysis"

Connected through hyperedge HE-8 (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"

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)