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 "beyond human parts dual part aligned representations for person re identification"):
  Abstract: Person re-identification is a challenging task due to various complex factors. Recent studies have attempted to integrate human parsing results or externally defined attributes to help capture human parts or important object regions. On the other hand, there still exist many useful contextual cues that do not fall into the scope of predefined human parts or attributes. In this paper, we address the missed contextual cues by exploiting both the accurate human parts and the coarse non-human parts. In our implementation, we apply a human parsing model to extract the binary human part masks \emph{and} a self-attention mechanism to capture the soft latent (non-human) part masks. We verify the effectiveness of our approach with new state-of-the-art performances on three challenging benchmarks: Market-1501, DukeMTMC-reID and CUHK03. Our implementation is available at this https URL.

Includes members:
  Member "interlaced sparse self attention for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")
  Member "aanet attribute attention network for person re identifications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "object contextual representations for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")
  Member "attribute aware pooling for pedestrian attribute recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cars continuous evolution for efficient neural architecture search")
  Member "improving person re identification by attribute and identity learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "attribute aware attention model for fine grained representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ghostnet more features from cheap operations")
  Member "devil in the details towards accurate single and multiple human parsing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "multi level factorisation net for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")

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)