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: "learning cross modal deep representations for robust pedestrian detection"
  Abstract: This paper presents a novel method for detecting pedestrians under adverse illumination conditions. Our approach relies on a novel cross-modality learning framework and it is based on two main phases. First, given a multimodal dataset, a deep convolutional network is employed to learn a non-linear mapping, modeling the relations between RGB and thermal data. Then, the learned feature representations are transferred to a second deep network, which receives as input an RGB image and outputs the detection results. In this way, features which are both discriminative and robust to bad illumination conditions are learned. Importantly, at test time, only the second pipeline is considered and no thermal data are required. Our extensive evaluation demonstrates that the proposed approach outperforms the state-of- the-art on the challenging KAIST multispectral pedestrian dataset and it is competitive with previous methods on the popular Caltech dataset.

Connected through hyperedge HE-1 (induced by source paper "how much real data do we actually need analyzing object detection performance using synthetic and real data"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"
    - "augmented lidar simulator for autonomous driving"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "efficient road lane marking detection with deep learning"
    - "few shot object detection via feature reweighting"

Connected through hyperedge HE-2 (induced by source paper "cooperative cross stream network for discriminative action representation"):
  with members:
    - "learning discriminative video representations using adversarial perturbations"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "on the effectiveness of task granularity for transfer learning"
    - "video representation learning using discriminative pooling"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-3 (induced by source paper "segmenting objects in day and night edge conditioned cnn for thermal image semantic segmentation"):
  with members:
    - "synthetic data generation for end to end thermal infrared tracking"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "rgb t object tracking benchmark and baseline"
    - "modulating early visual processing by language"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "learning visual reasoning without strong priors"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-4 (induced by source paper "synthetic data generation for end to end thermal infrared tracking"):
  with members:
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "good features to correlate for visual tracking"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "crest convolutional residual learning for visual tracking"
    - "learning background aware correlation filters for visual tracking"

Connected through hyperedge HE-5 (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"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"

Connected through hyperedge HE-6 (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"
    - "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"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-7 (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"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "repulsion loss detecting pedestrians in a crowd"

Connected through hyperedge HE-8 (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"
    - "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"
    - "multispectral deep neural networks for pedestrian 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)