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 "distraction aware feature learning for human attribute recognition via coarse to fine attention mechanism"):
  Abstract: Recently, Human Attribute Recognition (HAR) has become a hot topic due to its scientific challenges and application potentials, where localizing attributes is a crucial stage but not well handled. In this paper, we propose a novel deep learning approach to HAR, namely Distraction-aware HAR (Da-HAR). It enhances deep CNN feature learning by improving attribute localization through a coarse-to-fine attention mechanism. At the coarse step, a self-mask block is built to roughly discriminate and reduce distractions, while at the fine step, a masked attention branch is applied to further eliminate irrelevant regions. Thanks to this mechanism, feature learning is more accurate, especially when heavy occlusions and complex backgrounds exist. Extensive experiments are conducted on the WIDER-Attribute and RAP databases, and state-of-the-art results are achieved, demonstrating the effectiveness of the proposed approach.

Includes members:
  Member "dynamic curriculum learning for imbalanced data classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "forward and backward information retention for accurate binary neural networks")
  Member "attribute aware pooling for pedestrian attribute recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cars continuous evolution for efficient neural architecture search")
  Member "attribute aware attention model for fine grained representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ghostnet more features from cheap operations")
  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 "adaptive svm learning with privileged information for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hard pixel mining for depth privileged semantic segmentation")
  Member "hydraplus net attentive deep features for pedestrian analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")

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)