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: "multi directional multi level dual cross patterns for robust face recognition"
  Abstract: To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract “Multi-Directional Multi-Level Dual-Cross Patterns” (MDML-DCPs) from face images. Specifically, the MDML-DCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g., LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.

Connected through hyperedge HE-1 (induced by source paper "ldop local directional order pattern for robust face retrieval"):
  with members:
    - "local directional relation pattern for unconstrained and robust face retrieval"
    - "face retrieval using frequency decoded local descriptor"
    - "a comprehensive survey on pose invariant face recognition"

Connected through hyperedge HE-2 (induced by source paper "learning robust 3d face reconstruction and discriminative identity representation"):
  with members:
    - "deep transfer across domains for face antispoofing"
    - "learning generalizable and identity discriminative representations for face anti spoofing"
    - "joint 3d face reconstruction and dense face alignment from a single image with 2d assisted self supervised learning"
    - "face alignment in full pose range a 3d total solution"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "dense face alignment"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"

Connected through hyperedge HE-3 (induced by source paper "complexface a multi representation approach for image classification with small dataset"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"
    - "deep complex networks"
    - "a survey of sparse representation algorithms and applications"
    - "robust face recognition via multimodal deep face representation"
    - "sparsifying neural network connections for face recognition"
    - "deepid3 face recognition with very deep neural networks"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-4 (induced by source paper "extended local binary patterns for efficient and robust spontaneous facial micro expression recognition"):
  with members:
    - "shallow triple stream three dimensional cnn ststnet for micro expression recognition"
    - "spontaneous facial micro expression recognition using 3d spatiotemporal convolutional neural networks"
    - "a novel apex time network for cross dataset micro expression recognition"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "enriched long term recurrent convolutional network for facial micro expression recognition"
    - "a survey of automatic facial micro expression analysis databases methods and challenges"
    - "learning based video motion magnification"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"

Connected through hyperedge HE-5 (induced by source paper "multi fold gabor pca and ica filter convolution descriptor for face recognition"):
  with members:
    - "deepid3 face recognition with very deep neural networks"
    - "when face recognition meets with deep learning an evaluation of convolutional neural networks for face recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "a comprehensive survey on pose invariant face recognition"
    - "dctnet a simple learning free approach for face recognition"
    - "pcanet a simple deep learning baseline for image classification"
    - "effective face frontalization in unconstrained images"
    - "large scale strongly supervised ensemble metric learning with applications to face verification and retrieval"

Connected through hyperedge HE-6 (induced by source paper "local directional relation pattern for unconstrained and robust face retrieval"):
  with members:
    - "action2activity recognizing complex activities from sensor data"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "a comprehensive survey on pose invariant face recognition"
    - "the megaface benchmark 1 million faces for recognition at scale"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "coupled learning for facial deblur"):
  with members:
    - "a comprehensive survey on pose invariant face recognition"
    - "removing camera shake via weighted fourier burst accumulation"
    - "recent progress in image deblurring"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-8 (induced by source paper "from bow to cnn two decades of texture representation for texture classification"):
  with members:
    - "deep learning for generic object detection a survey"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "low rank bilinear pooling for fine grained classification"
    - "deep ten texture encoding network"
    - "approximate fisher kernels of non iid image models for image categorization"
    - "using filter banks in convolutional neural networks for texture classification"

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)