human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "illumination aware faster r cnn for robust multispectral pedestrian detection"):
  Abstract: Multispectral images of color-thermal pairs have shown more effective than a single color channel for pedestrian detection, especially under challenging illumination conditions. However, there is still a lack of studies on how to fuse the two modalities effectively. In this paper, we deeply compare six different convolutional network fusion architectures and analyse their adaptations, enabling a vanilla architecture to obtain detection performances comparable to the state-of-the-art results. Further, we discover that pedestrian detection confidences from color or thermal images are correlated with illumination conditions. With this in mind, we propose an Illumination-aware Faster R-CNN (IAF RCNN). Specifically, an Illumination-aware Network is introduced to give an illumination measure of the input image. Then we adaptively merge color and thermal sub-networks via a gate function defined over the illumination value. The experimental results on KAIST Multispectral Pedestrian Benchmark validate the effectiveness of the proposed IAF R-CNN.

Includes members:
  Member "learning cross modal deep representations for robust pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how much real data do we actually need analyzing object detection performance using synthetic and real data")
  Member "illuminating pedestrians via simultaneous detection segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pedestrian detection the elephant in the room")
  Member "citypersons a diverse dataset for pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adapted center and scale prediction more stable and more accurate")
  Member "repulsion loss detecting pedestrians in a crowd":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "how far are we from solving pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pedestrian detection the elephant in the room")
  Member "a unified multi scale deep convolutional neural network for fast object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "fused dnn a deep neural network fusion approach to fast and robust pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "baod budget aware object detection")
  Member "is faster r cnn doing well for pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "psc net learning part spatial co occurence for occluded pedestrian detection")

Question: Please tell me which class the source paper 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)