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 "fmt fusing multi task convolutional neural network for person search"):
  Abstract: Person search is to detect all persons and identify the query persons from detected persons in the image without proposals and bounding boxes, which is different from person re-identification. In this paper, we propose a fusing multi-task convolutional neural network(FMT-CNN) to tackle the correlation and heterogeneity of detection and re-identification with a single convolutional neural network. We focus on how the interplay of person detection and person re-identification affects the overall performance. We employ person labels in region proposal network to produce features for person re-identification and person detection network, which can improve the accuracy of detection and re-identification simultaneously. We also use a multiple loss to train our re-identification network. Experiment results on CUHK-SYSU Person Search dataset show that the performance of our proposed method is superior to state-of-the-art approaches in both mAP and top-1.

Includes members:
  Member "neural person search machines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "triplet online instance matching loss for person re identification")
  Member "end to end comparative attention networks for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "disassembling the dataset a camera alignment mechanism for multiple tasks in person re identification")
  Member "svdnet for pedestrian retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "learning deep feature representations with domain guided dropout for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "joint detection and identification feature learning for person search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "triplet online instance matching loss for person re identification")
  Member "learning to rank in person re identification with metric ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised person re identification by deep asymmetric metric embedding")
  Member "deep feature learning with relative distance comparison for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "filtered channel features for pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adapted center and scale prediction more stable and more accurate")

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)