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 "reaps towards better recognition of fine grained images by region attending and part sequencing"):
  Abstract: Fine-grained image recognition has been a hot research topic in computer vision due to its various applications. The-state-of-the-art is the part/region-based approaches that first localize discriminative parts/regions, and then learn their fine-grained features. However, these approaches have some inherent drawbacks: 1) the discriminative feature representation of an object is prone to be disturbed by complicated background; 2) it is unreasonable and inflexible to fix the number of salient parts, because the intended parts may be unavailable under certain circumstances due to occlusion or incompleteness, and 3) the spatial correlation among different salient parts has not been thoroughly exploited (if not completely neglected). To overcome these drawbacks, in this paper we propose a new, simple yet robust method by building part sequence model on the attended object region. Concretely, we first try to alleviate the background effect by using a region attention mechanism to generate the attended region from the original image. Then, instead of localizing different salient parts and extracting their features separately, we learn the part representation implicitly by applying a mapping function on the serialized features of the object. Finally, we combine the region attending network and the part sequence learning network into a unified framework that can be trained end-to-end with only image-level labels. Our extensive experiments on three fine-grained benchmarks show that the proposed method achieves the state of the art performance.

Includes members:
  Member "fast fine grained image classification via weakly supervised discriminative localization":
    also connected through other hyperedges:
      (none)
  Member "improved bilinear pooling with cnns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "low rank bilinear pooling for fine grained classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fine grained visual textual representation learning")
  Member "fully convolutional attention localization networks efficient attention localization for fine grained recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "mining discriminative triplets of patches for fine grained classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention convolutional binary neural tree for fine grained visual categorization")
  Member "compact bilinear pooling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "component analysis for visual question answering architectures")
  Member "bilinear cnn models for fine grained visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "spatial transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")

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)