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 "classification specific parts for improving fine grained visual categorization"):
  Abstract: Fine-grained visual categorization is a classification task for distinguishing categories with high intra-class and small inter-class variance. While global approaches aim at using the whole image for performing the classification, part-based solutions gather additional local information in terms of attentions or parts. We propose a novel classification-specific part estimation that uses an initial prediction as well as back-propagation of feature importance via gradient computations in order to estimate relevant image regions. The subsequently detected parts are then not only selected by a-posteriori classification knowledge, but also have an intrinsic spatial extent that is determined automatically. This is in contrast to most part-based approaches and even to available ground-truth part annotations, which only provide point coordinates and no additional scale information. We show in our experiments on various widely-used fine-grained datasets the effectiveness of the mentioned part selection method in conjunction with the extracted part features.

Includes members:
  Member "unsupervised part mining for fine grained image classification":
    also connected through other hyperedges:
      (none)
  Member "weakly supervised complementary parts models for fine grained image classification from the bottom up":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised attention pyramid convolutional neural network for fine grained visual classification")
  Member "large scale fine grained categorization and domain specific transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking the hyperparameters for fine tuning")
  Member "the inaturalist species classification and detection dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "bilinear cnn models for fine grained visual recognition":
    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 "neural activation constellations unsupervised part model discovery with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interpretable cnns for object classification")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "the unreasonable effectiveness of noisy data for fine grained recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way")

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)