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 "generalization ability of region proposal networks for multispectral person detection"):
  Abstract: Multispectral person detection aims at automatically localizing humans in images that consist of multiple spectral bands. Usually, the visual-optical (VIS) and the thermal infrared (IR) spectra are combined to achieve higher robustness for person detection especially in insufficiently illuminated scenes. This paper focuses on analyzing existing detection approaches for their generalization ability. Generalization is a key feature for machine learning based detection algorithms that are supposed to perform well across different datasets. Inspired by recent literature regarding person detection in the VIS spectrum, we perform a cross-validation study to empirically determine the most promising dataset to train a well-generalizing detector. Therefore, we pick one reference Deep Convolutional Neural Network (DCNN) architecture and three different multispectral datasets. The Region Proposal Network (RPN) originally introduced for object detection within the popular Faster R-CNN is chosen as a reference DCNN. The reason is that a stand-alone RPN is able to serve as a competitive detector for two-class problems such as person detection. Furthermore, current state-of-the-art approaches initially apply an RPN followed by individual classifiers. The three considered datasets are the KAIST Multispectral Pedestrian Benchmark including recently published improved annotations for training and testing, the Tokyo Multi-spectral Semantic Segmentation dataset, and the OSU Color-Thermal dataset including recently released annotations. The experimental results show that the KAIST Multispectral Pedestrian Benchmark with its improved annotations provides the best basis to train a DCNN with good generalization ability compared to the other two multispectral datasets. On average, this detection model achieves a log-average Miss Rate (MR) of 29.74 % evaluated on the reasonable test subsets of the three datasets.

Includes members:
  Member "multispectral pedestrian detection via simultaneous detection and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ps rcnn detecting secondary human instances in a crowd via primary object suppression")
  Member "generalisation in humans and deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an empirical evaluation on robustness and uncertainty of regularization methods")
  Member "illumination aware faster r cnn for robust multispectral pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multimodal end to end autonomous driving")
  Member "illuminating pedestrians via simultaneous detection segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "adapted center and scale prediction more stable and more accurate")
  Member "linear support tensor machine with lsk channels pedestrian detection in thermal infrared images":
    also connected through other hyperedges:
      (none)
  Member "how far are we from solving pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pedestrian detection the elephant in the room")
  Member "is faster r cnn doing well for pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)