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: "fusion of multispectral data through illumination aware deep neural networks for pedestrian detection"
  Abstract: Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g. security surveillance and autonomous driving). In this paper, we demonstrate illumination information encoded in multispectral images can be utilized to significantly boost performance of pedestrian detection. A novel illumination-aware weighting mechanism is present to accurately depict illumination condition of a scene. Such illumination information is incorporated into two-stream deep convolutional neural networks to learn multispectral human-related features under different illumination conditions (daytime and nighttime). Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy. Putting all of the pieces together, we present a powerful framework for multispectral pedestrian detection based on multi-task learning of illumination-aware pedestrian detection and semantic segmentation. Our proposed method is trained end-to-end using a well-designed multi-task loss function and outperforms state-of-the-art approaches on KAIST multispectral pedestrian dataset.

Connected through hyperedge HE-1 (induced by source paper "the cross modality disparity problem in multispectral pedestrian detection"):
  with members:
    - "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"
    - "what can help pedestrian detection"
    - "repulsion loss detecting pedestrians in a crowd"
    - "is faster r cnn doing well for pedestrian detection"
    - "ssd single shot multibox detector"
    - "multispectral deep neural networks for pedestrian detection"

Connected through hyperedge HE-2 (induced by source paper "multispectral pedestrian detection via simultaneous detection and segmentation"):
  with members:
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "citypersons a diverse dataset for pedestrian detection"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "fused dnn a deep neural network fusion approach to fast and robust pedestrian detection"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "fast r cnn"

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)