human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "impact of fully connected layers on performance of convolutional neural networks for image classification"
  Abstract: Abstract   The Convolutional Neural Networks (CNNs), in domains like computer vision, mostly reduced the need for handcrafted features due to its ability to learn the problem-specific features from the raw input data. However, the selection of dataset-specific CNN architecture, which mostly performed by either experience or expertise is a time-consuming and error-prone process. To automate the process of learning a CNN architecture, this paper attempts at finding the relationship between Fully Connected (FC) layers with some of the characteristics of the datasets. The CNN architectures, and recently datasets also, are categorized as deep, shallow, wide, etc. This paper tries to formalize these terms along with answering the following questions. (i) What is the impact of deeper/shallow architectures on the performance of the CNN w.r.t. FC layers?, (ii) How the deeper/wider datasets influence the performance of CNN w.r.t. FC layers?, and (iii) Which kind of architecture (deeper/shallower) is better suitable for which kind of (deeper/wider) datasets. To address these findings, we have performed experiments with four CNN architectures having different depths. The experiments are conducted by varying the number of FC layers. We used four widely used datasets including CIFAR-10, CIFAR-100, Tiny ImageNet, and CRCHistoPhenotypes to justify our findings in the context of image classification problem. The source code of this work is available at  https://github.com/shabbeersh/Impact-of-FC-layers .

Connected through hyperedge HE-1 (induced by source paper "looking glamorous vehicle re id in heterogeneous cameras networks with global and local attention"):
  with members:
    - "part guided attention learning for vehicle re identification"
    - "attributes guided feature learning for vehicle re identification"
    - "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"
    - "the state of sparsity in deep neural networks"
    - "group normalization"
    - "ram a region aware deep model for vehicle re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"

Connected through hyperedge HE-2 (induced by source paper "autofcl automatically tuning fully connected layers for transfer learning"):
  with members:
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep residual learning for image recognition"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "rethinking the inception architecture for computer vision"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "small accurate and fast vehicle re id on the edge the safr approach"):
  with members:
    - "the state of sparsity in deep neural networks"
    - "ram a region aware deep model for vehicle re identification"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "vehicle re identification using quadruple directional deep learning features"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "exploring sparsity in recurrent neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-4 (induced by source paper "an information rich sampling technique over spatio temporal cnn for classification of human actions in videos"):
  with members:
    - "lattice long short term memory for human action recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "adascan adaptive scan pooling in deep convolutional neural networks for human action recognition in videos"
    - "adam a method for stochastic optimization"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"

Question: Please tell me which class the center node 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)