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 "growing interpretable part graphs on convnets via multi shot learning"):
  Abstract: This paper proposes a learning strategy that extracts object-part concepts from a pre-trained convolutional neural network (CNN), in an attempt to 1) explore explicit semantics hidden in CNN units and 2) gradually grow a semantically interpretable graphical model on the pre-trained CNN for hierarchical object understanding. Given part annotations on very few (e.g., 3-12) objects, our method mines certain latent patterns from the pre-trained CNN and associates them with different semantic parts. We use a four-layer And-Or graph to organize the mined latent patterns, so as to clarify their internal semantic hierarchy. Our method is guided by a small number of part annotations, and it achieves superior performance (about 13%-107% improvement) in part center prediction on the PASCAL VOC and ImageNet datasets.

Includes members:
  Member "inverting visual representations with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interpretable cnns for object classification")
  Member "unsupervised learning on neural network outputs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "classifier and exemplar synthesis for zero shot learning")
  Member "what happened to my dog in that network unraveling top down generators in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mining interpretable aog representations from convolutional networks via active question answering")
  Member "neural activation constellations unsupervised part model discovery with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interpretable cnns for object classification")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "deep learning for semantic part segmentation with high level guidance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning structure via consensus for face segmentation and parsing")
  Member "understanding deep features with computer generated imagery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interpretable cnns for object classification")
  Member "fast r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)