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: "evaluating deep convolutional neural networks for material classification"
  Abstract: Determining the material category of a surface from an image is a demanding task in perception that is drawing increasing attention. Following the recent remarkable results achieved for image classification and object detection utilising Convolutional Neural Networks (CNNs), we empirically study material classification of everyday objects employing these techniques. More specifically, we conduct a rigorous evaluation of how state-of-the art CNN architectures compare on a common ground over widely used material databases. Experimental results on three challenging material databases show that the best performing CNN architectures can achieve up to 94.99\% mean average precision when classifying materials.

Connected through hyperedge HE-1 (induced by source paper "assessing capsule networks with biased data"):
  with members:
    - "capsule networks for low data transfer learning"
    - "impact of biases in big data"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"
    - "dynamic routing between capsules"
    - "emnist an extension of mnist to handwritten letters"
    - "quad networks unsupervised learning to rank for interest point detection"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "an empirical evaluation of current convolutional architectures ability to manage nuisance location and scale variability"

Connected through hyperedge HE-2 (induced by source paper "exploring object centric and scene centric cnn features and their complementarity for human rights violations recognition in images"):
  with members:
    - "a paradigm shift detecting human rights violations through web images"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"
    - "material recognition in the wild with the materials in context database"
    - "caffe convolutional architecture for fast feature embedding"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "material classification using neural networks"):
  with members:
    - "transfer learning for material classification using convolutional networks"
    - "what makes imagenet good for transfer learning"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "material recognition in the wild with the materials in context database"
    - "imagenet large scale visual recognition challenge"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-4 (induced by source paper "material classification in the wild do synthesized training data generalise better than real world training data"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"
    - "material recognition in the wild with the materials in context database"
    - "caffe convolutional architecture for fast feature embedding"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "visualizing and understanding convolutional networks"

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)