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 "improving pedestrian attribute recognition with weakly supervised multi scale attribute specific localization"):
  Abstract: Pedestrian attribute recognition has been an emerging research topic in the area of video surveillance. To predict the existence of a particular attribute, it is demanded to localize the regions related to the attribute. However, in this task, the region annotations are not available. How to carve out these attribute-related regions remains challenging. Existing methods applied attribute-agnostic visual attention or heuristic body-part localization mechanisms to enhance the local feature representations, while neglecting to employ attributes to define local feature areas. We propose a flexible Attribute Localization Module (ALM) to adaptively discover the most discriminative regions and learns the regional features for each attribute at multiple levels. Moreover, a feature pyramid architecture is also introduced to enhance the attribute-specific localization at low-levels with high-level semantic guidance. The proposed framework does not require additional region annotations and can be trained end-to-end with multi-level deep supervision. Extensive experiments show that the proposed method achieves state-of-the-art results on three pedestrian attribute datasets, including PETA, RAP, and PA-100K.

Includes members:
  Member "pedestrian attribute recognition a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved hard example mining by discovering attribute based hard person identity")
  Member "harmonious attention network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "transferable joint attribute identity deep learning for unsupervised person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "deep imbalanced attribute classification using visual attention aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "localization guided learning for pedestrian attribute recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pedestrian attribute recognition a survey")
  Member "resource aware person re identification across multiple resolutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning shape representations for clothing variations in person re identification")
  Member "multi label image recognition by recurrently discovering attentional regions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pedestrian attribute recognition a survey")
  Member "squeeze and excitation networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)