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: "material recognition from local appearance in global context"
  Abstract: Recognition of materials has proven to be a challenging problem due to the wide variation in appearance within and between categories. Global image context, such as where the material is or what object it makes up, can be crucial to recognizing the material. Existing methods, however, operate on an implicit fusion of materials and context by using large receptive fields as input (i.e., large image patches). Many recent material recognition methods treat materials as yet another set of labels like objects. Materials are, however, fundamentally different from objects as they have no inherent shape or defined spatial extent. Approaches that ignore this can only take advantage of limited implicit context as it appears during training. We instead show that recognizing materials purely from their local appearance and integrating separately recognized global contextual cues including objects and places leads to superior dense, per-pixel, material recognition. We achieve this by training a fully-convolutional material recognition network end-to-end with only material category supervision. We integrate object and place estimates to this network from independent CNNs. This approach avoids the necessity of preparing an impractically-large amount of training data to cover the product space of materials, objects, and scenes, while fully leveraging contextual cues for dense material recognition. Furthermore, we perform a detailed analysis of the effects of context granularity, spatial resolution, and the network level at which we introduce context. On a recently introduced comprehensive and diverse material database \cite{Schwartz2016}, we confirm that our method achieves state-of-the-art accuracy with significantly less training data compared to past methods.

Connected through hyperedge HE-1 (induced by source paper "a similarity measure for material appearance"):
  with members:
    - "learning icons appearance similarity"
    - "on the convergence of adam and beyond"
    - "brdf estimation of complex materials with nested learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "recognizing material properties from images"
    - "revisiting temporal modeling for video based person reid"
    - "gaussian material synthesis"
    - "a lightweight approach for on the fly reflectance estimation"

Connected through hyperedge HE-2 (induced by source paper "context encoding for semantic segmentation"):
  with members:
    - "path aggregation network for instance segmentation"
    - "multi style generative network for real time transfer"
    - "dilated residual networks"
    - "squeeze and excitation networks"
    - "megdet a large mini batch object detector"
    - "rethinking atrous convolution for semantic image segmentation"
    - "bridging category level and instance level semantic image segmentation"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep texture manifold for ground terrain recognition"):
  with members:
    - "island loss for learning discriminative features in facial expression recognition"
    - "exemplar centered supervised shallow parametric data embedding"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "hidden two stream convolutional networks for action recognition"
    - "generative adversarial network based synthesis of visible faces from polarimetric thermal faces"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "deep ten texture encoding network"
    - "a 4d light field dataset and cnn architectures for material recognition"

Connected through hyperedge HE-4 (induced by source paper "a fully end to end deep learning approach for real time simultaneous 3d reconstruction and material recognition"):
  with members:
    - "weakly supervised dcnn for rgb d object recognition in real world applications which lack large scale annotated training data"
    - "geometry informed material recognition"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "conditional random fields as recurrent neural networks"
    - "learning deconvolution network for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "material recognition in the wild with the materials in context database"

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)