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 "real time fine grained categorization with accuracy and interpretability"):
  Abstract: A well-designed fine-grained categorization system usually has three contradictory requirements: accuracy (the ability to identify objects among subordinate categories); interpretability (the ability to provide human-understandable explanation of recognition system behavior); and efficiency (the speed of the system). To handle the trade-off between accuracy and interpretability, we propose a novel "Deeper Part-Stacked CNN" architecture armed with interpretability by modeling subtle differences between object parts. The proposed architecture consists of a part localization network, a two-stream classification network that simultaneously encodes object-level and part-level cues, and a feature vectors fusion component. Specifically, the part localization network is implemented by exploring a new paradigm for key point localization that first samples a small number of representable pixels and then determine their labels via a convolutional layer followed by a softmax layer. We also use a cropping layer to extract part features and propose a scale mean-max layer for feature fusion learning. Experimentally, our proposed method outperform state-of-the-art approaches both in part localization task and classification task on Caltech-UCSD Birds-200-2011. Moreover, by adopting a set of sharing strategies between the computation of multiple object parts, our single model is fairly efficient running at 32 frames/sec.

Includes members:
  Member "convolutional pose machines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "deep deformation network for object landmark localization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses")
  Member "fine grained pose prediction normalization and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")
  Member "spatial transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformation based adversarial video prediction on large scale data")
  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 "deepm a deep part based model for object detection and semantic part localization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  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)