human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep texture manifold for ground terrain recognition"):
  Abstract: We present a texture network called Deep Encoding Pooling Network (DEP) for the task of ground terrain recognition. Recognition of ground terrain is an important task in establishing robot or vehicular control parameters, as well as for localization within an outdoor environment. The architecture of DEP integrates orderless texture details and local spatial information and the performance of DEP surpasses state-of-the-art methods for this task. The GTOS database (comprised of over 30,000 images of 40 classes of ground terrain in outdoor scenes) enables supervised recognition. For evaluation under realistic conditions, we use test images that are not from the existing GTOS dataset, but are instead from hand-held mobile phone videos of similar terrain. This new evaluation dataset, GTOS-mobile, consists of 81 videos of 31 classes of ground terrain such as grass, gravel, asphalt and sand. The resultant network shows excellent performance not only for GTOS-mobile, but also for more general databases (MINC and DTD). Leveraging the discriminant features learned from this network, we build a new texture manifold called DEP-manifold. We learn a parametric distribution in feature space in a fully supervised manner, which gives the distance relationship among classes and provides a means to implicitly represent ambiguous class boundaries. The source code and database are publicly available.

Includes members:
  Member "island loss for learning discriminative features in facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "region attention networks for pose and occlusion robust facial expression recognition")
  Member "exemplar centered supervised shallow parametric data embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "supervised dimensionality reduction and visualization using centroid encoder")
  Member "mdnet a semantically and visually interpretable medical image diagnosis network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "clara clinical report auto completion")
  Member "hidden two stream convolutional networks for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition")
  Member "generative adversarial network based synthesis of visible faces from polarimetric thermal faces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polarimetric thermal to visible face verification via self attention guided synthesis")
  Member "a recurrent encoder decoder network for sequential face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "deep ten texture encoding network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "histogram layers for texture analysis")
  Member "a 4d light field dataset and cnn architectures for material recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "style transfer for light field photography")

Question: Please tell me which class the source paper 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)