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 "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.

Includes members:
  Member "fusion of multispectral data through illumination aware deep neural networks for pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the cross modality disparity problem in multispectral pedestrian detection")
  Member "illumination aware faster r cnn for robust multispectral pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multimodal end to end autonomous driving")
  Member "illuminating pedestrians via simultaneous detection segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "adapted center and scale prediction more stable and more accurate")
  Member "the cityscapes dataset for semantic urban scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  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 "tensorflow large scale machine learning on heterogeneous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)