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 "attribute aware pooling for pedestrian attribute recognition"):
  Abstract: This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm. Existing vanilla CNNs cannot be straightforwardly applied to handle multi-attribute data because of the larger label space as well as the attribute entanglement and correlations. We tackle these challenges that hampers the development of CNNs for multi-attribute classification by fully exploiting the correlation between different attributes. The multi-branch architecture is adopted for fucusing on attributes at different regions. Besides the prediction based on each branch itself, context information of each branch are employed for decision as well. The attribute aware pooling is developed to integrate both kinds of information. Therefore, attributes which are indistinct or tangled with others can be accurately recognized by exploiting the context information. Experiments on benchmark datasets demonstrate that the proposed pooling method appropriately explores and exploits the correlations between attributes for the pedestrian attribute recognition.

Includes members:
  Member "attribute aware attention model for fine grained representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "pedestrian attribute recognition a survey")
  Member "autoencoder inspired unsupervised feature selection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attribute aware attention model for fine grained representation learning")
  Member "hydraplus net attentive deep features for pedestrian analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "beyond part models person retrieval with refined part pooling and a strong convolutional baseline":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "fully adaptive feature sharing in multi task networks with applications in person attribute classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mti net multi scale task interaction networks for multi task learning")

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)