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: "multispectral deep neural networks for pedestrian detection"
  Abstract: Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two-branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle-level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.

Connected through hyperedge HE-1 (induced by source paper "box level segmentation supervised deep neural networks for accurate and real time multispectral pedestrian detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "unbiasing semantic segmentation for robot perception using synthetic data feature transfer"
    - "cornernet detecting objects as paired keypoints"
    - "multispectral pedestrian detection via simultaneous detection and segmentation"
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"
    - "focal loss for dense object detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "citypersons a diverse dataset for pedestrian detection"

Connected through hyperedge HE-2 (induced by source paper "weakly aligned cross modal learning for multispectral pedestrian detection"):
  with members:
    - "box level segmentation supervised deep neural networks for accurate and real time multispectral pedestrian detection"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "multispectral pedestrian detection via simultaneous detection and segmentation"
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"

Connected through hyperedge HE-3 (induced by source paper "gfd ssd gated fusion double ssd for multispectral pedestrian detection"):
  with members:
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"
    - "fssd feature fusion single shot multibox detector"
    - "focal loss for dense object detection"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dssd deconvolutional single shot detector"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "is faster r cnn doing well for pedestrian detection"

Connected through hyperedge HE-4 (induced by source paper "unsupervised domain adaptation for multispectral pedestrian detection"):
  with members:
    - "box level segmentation supervised deep neural networks for accurate and real time multispectral pedestrian detection"
    - "multispectral pedestrian detection via simultaneous detection and segmentation"
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"
    - "unsupervised deep domain adaptation for pedestrian detection"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"

Connected through hyperedge HE-5 (induced by source paper "pedestrian detection in thermal images using saliency maps"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "aggregated residual transformations for deep neural networks"
    - "fused dnn a deep neural network fusion approach to fast and robust pedestrian detection"
    - "is faster r cnn doing well for pedestrian detection"

Connected through hyperedge HE-6 (induced by source paper "the cross modality disparity problem in multispectral pedestrian detection"):
  with members:
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "fusion of multispectral data through illumination aware deep neural networks for pedestrian detection"
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "what can help pedestrian detection"
    - "repulsion loss detecting pedestrians in a crowd"
    - "is faster r cnn doing well for pedestrian detection"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-7 (induced by source paper "borrow from anywhere pseudo multi modal object detection in thermal imagery"):
  with members:
    - "yolov3 an incremental improvement"
    - "drit diverse image to image translation via disentangled representations"
    - "multi channel cnn based object detection for enhanced situation awareness"
    - "single shot refinement neural network for object detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "dualgan unsupervised dual learning for image to image translation"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "unpaired thermal to visible spectrum transfer using adversarial training"):
  with members:
    - "colorization as a proxy task for visual understanding"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "toward multimodal image to image translation"
    - "infrared colorization using deep convolutional neural networks"
    - "image to image translation with conditional adversarial networks"
    - "colorful image colorization"

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)