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 pedestrian detection via simultaneous detection and segmentation"
  Abstract: Multispectral pedestrian detection has attracted increasing attention from the research community due to its crucial competence for many around-the-clock applications (e.g., video surveillance and autonomous driving), especially under insufficient illumination conditions. We create a human baseline over the KAIST dataset and reveal that there is still a large gap between current top detectors and human performance. To narrow this gap, we propose a network fusion architecture, which consists of a multispectral proposal network to generate pedestrian proposals, and a subsequent multispectral classification network to distinguish pedestrian instances from hard negatives. The unified network is learned by jointly optimizing pedestrian detection and semantic segmentation tasks. The final detections are obtained by integrating the outputs from different modalities as well as the two stages. The approach significantly outperforms state-of-the-art methods on the KAIST dataset while remain fast. Additionally, we contribute a sanitized version of training annotations for the KAIST dataset, and examine the effects caused by different kinds of annotation errors. Future research of this problem will benefit from the sanitized version which eliminates the interference of annotation errors.

Connected through hyperedge HE-1 (induced by source paper "ps rcnn detecting secondary human instances in a crowd via primary object suppression"):
  with members:
    - "widerperson a diverse dataset for dense pedestrian detection in the wild"
    - "mask guided attention network for occluded pedestrian detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "soft nms improving object detection with one line of code"
    - "cascade r cnn delving into high quality object detection"
    - "repulsion loss detecting pedestrians in a crowd"

Connected through hyperedge HE-2 (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"
    - "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"
    - "what can help pedestrian detection"

Connected through hyperedge HE-3 (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"
    - "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"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"

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"
    - "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"
    - "repulsion loss detecting pedestrians in a crowd"

Connected through hyperedge HE-5 (induced by source paper "generalization ability of region proposal networks for multispectral person detection"):
  with members:
    - "generalisation in humans and deep neural networks"
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "citypersons a diverse dataset for pedestrian detection"
    - "linear support tensor machine with lsk channels pedestrian detection in thermal infrared images"
    - "how far are we from solving pedestrian detection"
    - "is faster r cnn doing well for pedestrian detection"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)