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: "deep heterogeneous feature fusion for template based face recognition"
  Abstract: Although deep learning has yielded impressive performance for face recognition, many studies have shown that different networks learn different feature maps: while some networks are more receptive to pose and illumination others appear to capture more local information. Thus, in this work, we propose a deep heterogeneous feature fusion network to exploit the complementary information present in features generated by different deep convolutional neural networks (DCNNs) for template-based face recognition, where a template refers to a set of still face images or video frames from different sources which introduces more blur, pose, illumination and other variations than traditional face datasets. The proposed approach efficiently fuses the discriminative information of different deep features by 1) jointly learning the non-linear high-dimensional projection of the deep features and 2) generating a more discriminative template representation which preserves the inherent geometry of the deep features in the feature space. Experimental results on the IARPA Janus Challenge Set 3 (Janus CS3) dataset demonstrate that the proposed method can effectively improve the recognition performance. In addition, we also present a series of covariate experiments on the face verification task for in-depth qualitative evaluations for the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "synthesis of high quality visible faces from polarimetric thermal faces using generative adversarial networks"):
  with members:
    - "polarimetric thermal to visible face verification via attribute preserved synthesis"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deep cross polarimetric thermal to visible face recognition"
    - "disentangled variational representation for heterogeneous face recognition"
    - "context encoding for semantic segmentation"
    - "densely connected pyramid dehazing network"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-2 (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-3 (induced by source paper "side information for face completion a robust pca approach"):
  with members:
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "arcface additive angular margin loss for deep face recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "multicolumn networks for face recognition"
    - "comparator networks"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-4 (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"
    - "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"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-5 (induced by source paper "neurotreenet a new method to explore horizontal expansion network"):
  with members:
    - "enhancement of ssd by concatenating feature maps for object detection"
    - "enhanced deep residual networks for single image super resolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "split brain autoencoders unsupervised learning by cross channel prediction"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "multicolumn networks for face recognition"):
  with members:
    - "vggface2 a dataset for recognising faces across pose and age"
    - "a simple neural network module for relational reasoning"
    - "learning to compare relation network for few shot learning"
    - "neural aggregation network for video face recognition"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "umdfaces an annotated face dataset for training deep networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-7 (induced by source paper "cross domain visual recognition via domain adaptive dictionary learning"):
  with members:
    - "deep regionlets for object detection"
    - "return of frustratingly easy domain adaptation"
    - "learning transferable features with deep adaptation networks"
    - "deep domain confusion maximizing for domain invariance"
    - "domain adaptive neural networks for object recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "information theoretical learning of discriminative clusters for unsupervised domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "emotion detection on tv show transcripts with sequence based convolutional neural networks"):
  with members:
    - "wassa 2017 shared task on emotion intensity"
    - "a multichannel convolutional neural network for cross language dialog state tracking"
    - "convolutional sequence to sequence learning"
    - "attentive pooling networks"
    - "recurrent neural network for text classification with multi task learning"
    - "show attend and tell neural image caption generation with visual attention"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "teaching machines to read and comprehend"

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)