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: "sparse radial sampling lbp for writer identification"
  Abstract: In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features.

Connected through hyperedge HE-1 (induced by source paper "deep adaptive learning for writer identification based on single handwritten word images"):
  with members:
    - "an overview of multi task learning in deep neural networks"
    - "self transfer learning for fully weakly supervised object localization"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "cross stitch networks for multi task learning"
    - "deepwriter a multi stream deep cnn for text independent writer identification"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "semi supervised feature learning for improving writer identification"):
  with members:
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "unsupervised feature learning for writer identification and writer retrieval"
    - "deepwriter a multi stream deep cnn for text independent writer identification"
    - "a siamese long short term memory architecture for human re identification"
    - "convolutional patch representations for image retrieval an unsupervised approach"
    - "deep residual learning for image recognition"
    - "semi supervised learning with ladder networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "offline writer identification based on the path signature feature"):
  with members:
    - "deep adaptive learning for writer identification based on single handwritten word images"
    - "toward high performance online hccr"
    - "developing the path signature methodology and its application to landmark based human action recognition"
    - "leveraging the path signature for skeleton based human action recognition"
    - "learning spatial semantic context with fully convolutional recurrent network for online handwritten chinese text recognition"
    - "deepwriterid an end to end online text independent writer identification system"
    - "rotation invariants of two dimensional curves based on iterated integrals"

Connected through hyperedge HE-4 (induced by source paper "icdar 2019 competition on image retrieval for historical handwritten documents"):
  with members:
    - "offline writer identification based on the path signature feature"

Connected through hyperedge HE-5 (induced by source paper "non deterministic behavior of ranking based metrics when evaluating embeddings"):
  with members:
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "unsupervised feature learning for writer identification and writer retrieval"):
  with members:
    - "identity mappings in deep residual networks"
    - "deepwriter a multi stream deep cnn for text independent writer identification"
    - "convolutional patch representations for image retrieval an unsupervised approach"
    - "deep residual learning for image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "discriminative unsupervised feature learning with exemplar convolutional neural networks"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-7 (induced by source paper "improving patch based scene text script identification with ensembles of conjoined networks"):
  with members:
    - "a fine grained approach to scene text script identification"
    - "automatic script identification in the wild"
    - "a fast hierarchical method for multi script and arbitrary oriented scene text extraction"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "a fine grained approach to scene text script identification"):
  with members:
    - "automatic script identification in the wild"
    - "a fast hierarchical method for multi script and arbitrary oriented scene text extraction"

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)