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: "pooling faces template based face recognition with pooled face images"
  Abstract: We propose a novel approach to template based face recognition. Our dual goal is to both increase recognition accuracy and reduce the computational and storage costs of template matching. To do this, we leverage on an approach which was proven effective in many other domains, but, to our knowledge, never fully explored for face images: average pooling of face photos. We show how (and why!) the space of a template's images can be partitioned and then pooled based on image quality and head pose and the effect this has on accuracy and template size. We perform extensive tests on the IJB-A and Janus CS2 template based face identification and verification benchmarks. These show that not only does our approach outperform published state of the art despite requiring far fewer cross template comparisons, but also, surprisingly, that image pooling performs on par with deep feature pooling.

Connected through hyperedge HE-1 (induced by source paper "multi prototype networks for unconstrained set based face recognition"):
  with members:
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "cosface large margin cosine loss for deep face recognition"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for 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"

Connected through hyperedge HE-2 (induced by source paper "recurrent embedding aggregation network for video face recognition"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "multi prototype networks for unconstrained set based face recognition"
    - "spatial temporal recurrent neural network for emotion recognition"
    - "video face recognition component wise feature aggregation network c fan"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "ghostvlad for set based face recognition"
    - "multicolumn networks for face recognition"

Connected through hyperedge HE-3 (induced by source paper "attentional feature pair relation networks for accurate face recognition"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "pairwise relational networks for face recognition"
    - "comparator networks"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-4 (induced by source paper "video face recognition component wise feature aggregation network c fan"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "quality aware network for set to set recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "deepvisage making face recognition simple yet with powerful generalization skills"

Connected through hyperedge HE-5 (induced by source paper "ghostvlad for set based face recognition"):
  with members:
    - "multicolumn networks for face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "comparator networks"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "quality aware network for set to set recognition"
    - "squeeze and excitation networks"

Connected through hyperedge HE-6 (induced by source paper "convolutional point set representation a convolutional bridge between a densely annotated image and 3d face alignment"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "densepose dense human pose estimation in the wild"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "generative face completion"
    - "learning dense facial correspondences in unconstrained images"
    - "pose invariant face alignment with a single cnn"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "3d morphable models as spatial transformer 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"
    - "toward end to end face recognition through alignment learning"
    - "do we really need to collect millions of faces for effective face recognition"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "expnet landmark free deep 3d facial expressions"):
  with members:
    - "extreme 3d face reconstruction looking past occlusions"
    - "faceposenet making a case for landmark free face alignment"
    - "do we really need to collect millions of faces for effective face recognition"
    - "a multi scale cascade fully convolutional network face detector"
    - "fitting a 3d morphable model to edges a comparison between hard and soft correspondences"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "learning detailed face reconstruction from a single image"
    - "convolutional experts constrained local model for facial landmark detection"

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)