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 "localization guided learning for pedestrian attribute recognition"):
  Abstract: Pedestrian attribute recognition has attracted many attentions due to its wide applications in scene understanding and person analysis from surveillance videos. Existing methods try to use additional pose, part or viewpoint information to complement the global feature representation for attribute classification. However, these methods face difficulties in localizing the areas corresponding to different attributes. To address this problem, we propose a novel Localization Guided Network which assigns attribute-specific weights to local features based on the affinity between proposals pre-extracted proposals and attribute locations. The advantage of our model is that our local features are learned automatically for each attribute and emphasized by the interaction with global features. We demonstrate the effectiveness of our Localization Guided Network on two pedestrian attribute benchmarks (PA-100K and RAP). Our result surpasses the previous state-of-the-art in all five metrics on both datasets.

Includes members:
  Member "tell me where to look guided attention inference network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explainable deep classification models for domain generalization")
  Member "hydraplus net attentive deep features for pedestrian analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "attribute recognition by joint recurrent learning of context and correlation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "im2flow motion hallucination from static images for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ego pose estimation and forecasting as real time pd control")
  Member "deep view sensitive pedestrian attribute inference in an end to end model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pedestrian attribute recognition a survey")
  Member "generative adversarial models for people attribute recognition in surveillance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pedestrian attribute recognition a survey")
  Member "hide and seek forcing a network to be meticulous for weakly supervised object and action localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")
  Member "attribute recognition from adaptive parts":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pedestrian attribute recognition a survey")

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)