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: "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.

Connected through hyperedge HE-1 (induced by source paper "histogram layers for texture analysis"):
  with members:
    - "learnable histogram statistical context features for deep neural networks"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "texture classification in extreme scale variations using ganet"
    - "deep ten texture encoding network"
    - "a theoretical analysis of deep neural networks for texture classification"
    - "deep residual learning for image recognition"
    - "material recognition in the wild with the materials in context database"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "fine grained recognition accounting for subtle differences between similar classes"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "multi attention multi class constraint for fine grained image recognition"
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "rethinking feature distribution for loss functions in image classification"
    - "pairwise confusion for fine grained visual classification"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"

Connected through hyperedge HE-3 (induced by source paper "one shot texture retrieval with global context metric"):
  with members:
    - "mirrorgan learning text to image generation by redescription"
    - "meta learning for semi supervised few shot classification"
    - "one shot texture segmentation"
    - "annotation free and one shot learning for instance segmentation of homogeneous object clusters"
    - "the 2018 davis challenge on video object segmentation"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "one shot learning for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "feature level and model level audiovisual fusion for emotion recognition in the wild"):
  with members:
    - "identity free facial expression recognition using conditional generative adversarial network"
    - "probabilistic attribute tree in convolutional neural networks for facial expression recognition"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "island loss for learning discriminative features in facial expression recognition"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "optimizing filter size in convolutional neural networks for facial action unit recognition"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-5 (induced by source paper "multi level texture encoding and representation multer based on deep neural networks"):
  with members:
    - "deep ten texture encoding network"
    - "deep residual learning for image recognition"
    - "material recognition in the wild with the materials in context database"
    - "imagenet large scale visual recognition challenge"
    - "describing textures in the wild"

Connected through hyperedge HE-6 (induced by source paper "towards universal representation for unseen action recognition"):
  with members:
    - "alternative semantic representations for zero shot human action recognition"
    - "deep local video feature for action recognition"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"
    - "the kinetics human action video dataset"
    - "guided optical flow learning"
    - "image de raining using a conditional generative adversarial network"
    - "spatial aware object embeddings for zero shot localization and classification of actions"

Connected through hyperedge HE-7 (induced by source paper "gated transfer network for transfer learning"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "towards universal representation for unseen action recognition"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deep local video feature for action recognition"
    - "hidden two stream convolutional networks for action recognition"
    - "squeeze and excitation networks"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"

Connected through hyperedge HE-8 (induced by source paper "random temporal skipping for multirate video analysis"):
  with members:
    - "learning optical flow via dilated networks and occlusion reasoning"
    - "towards universal representation for unseen action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep local video feature for action recognition"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"
    - "the kinetics human action video dataset"

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)