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: "friction from reflectance deep reflectance codes for predicting physical surface properties from one shot in field reflectance"
  Abstract: Images are the standard input for vision algorithms, but one-shot infield reflectance measurements are creating new opportunities for recognition and scene understanding. In this work, we address the question of what reflectance can reveal about materials in an efficient manner. We go beyond the question of recognition and labeling and ask the question: What intrinsic physical properties of the surface can be estimated using reflectance? We introduce a framework that enables prediction of actual friction values for surfaces using one-shot reflectance measurements. This work is a first of its kind vision-based friction estimation. We develop a novel representation for reflectance disks that capture partial BRDF measurements instantaneously. Our method of deep reflectance codes combines CNN features and fisher vector pooling with optimal binary embedding to create codes that have sufficient discriminatory power and have important properties of illumination and spatial invariance. The experimental results demonstrate that reflectance can play a new role in deciphering the underlying physical properties of real-world scenes.

Connected through hyperedge HE-1 (induced by source paper "material segmentation of multi view satellite imagery"):
  with members:
    - "context encoding for semantic segmentation"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "stacked hourglass networks for human pose estimation"
    - "learning to push by grasping using multiple tasks for effective learning"
    - "deep ten texture encoding network"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "differential angular imaging for material recognition"):
  with members:
    - "deep ten texture encoding network"
    - "geometry informed material recognition"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"
    - "reflectance hashing for material recognition"

Connected through hyperedge HE-3 (induced by source paper "deep ten texture encoding network"):
  with members:
    - "identity mappings in deep residual networks"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "differential angular imaging for material recognition"
    - "deep filter banks for texture recognition description and segmentation"
    - "bilinear cnn models for fine grained visual recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "deep residual learning for image recognition"

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)