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 "improved hard example mining by discovering attribute based hard person identity"):
  Abstract: In this paper, we propose Hard Person Identity Mining (HPIM) that attempts to refine the hard example mining to improve the exploration efficacy in person re-identification. It is motivated by following observation: the more attributes some people share, the more difficult to separate their identities. Based on this observation, we develop HPIM via a transferred attribute describer, a deep multi-attribute classifier trained from the source noisy person attribute datasets. We encode each image into the attribute probabilistic description in the target person re-ID dataset. Afterwards in the attribute code space, we consider each person as a distribution to generate his view-specific attribute codes in different practical scenarios. Hence we estimate the person-specific statistical moments from zeroth to higher order, which are further used to calculate the central moment discrepancies between persons. Such discrepancy is a ground to choose hard identity to organize proper mini-batches, without concerning the person representation changing in metric learning. It presents as a complementary tool of hard example mining, which helps to explore the global instead of the local hard example constraint in the mini-batch built by randomly sampled identities. Extensive experiments on two person re-identification benchmarks validated the effectiveness of our proposed algorithm.

Includes members:
  Member "fmt fusing multi task convolutional neural network for person search":
    also connected through other hyperedges:
      (none)
  Member "pedestrian attribute recognition a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving pedestrian attribute recognition with weakly supervised multi scale attribute specific localization")
  Member "auto reid searching for a part aware convnet for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "searching central difference convolutional networks for face anti spoofing")
  Member "dense feature aggregation and pruning for rgbt tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "m 5 l multi modal multi margin metric learning for rgbt tracking")
  Member "improving person re identification by attribute and identity learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "in defense of the triplet loss for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "a discriminatively learned cnn embedding for person reidentification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "a fast rcnn hard positive generation via adversary for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on leveraging pretrained gans for limited data generation")

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)