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: "discovering perceptual attributes in a deep local material recognition network"
  Abstract: Perceptual material attributes, intrinsic visual properties of materials, are being studied in parallel in computer vision and human vision. In neuroscience, perceptual attributes are shown to be an integral part of the human neural response during material recognition. In computer vision, however, they are merely an intermediate representation of materials and not integrated into the recognition process. In this paper, we show that perceptual material attributes can indeed be found inside a framework for local, patch-based, object-independent material recognition. We introduce a new CNN architecture, the material attribute-category CNN (MAC-CNN), that uses deep weak supervision to simultaneously classify materials and discover per-pixel perceptual attributes. We show that these attributes conform with past semantic material attributes and enhance recognition of novel materials. We also introduce an extensive new database for local material recognition. Our results show that the internal representation of the MAC-CNN generalizes well and agrees with human perception, which has potential implications for our understanding of human material perception as well as applications in object recognition.

Connected through hyperedge HE-1 (induced by source paper "material recognition from local appearance in global context"):
  with members:
    - "deeply recursive convolutional network for image super resolution"
    - "human pose estimation with iterative error feedback"
    - "integrating deep features for material recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)