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: "hwnet v2 an efficient word image representation for handwritten documents"
  Abstract: We present a framework for learning efficient holistic representation for handwritten word images. The proposed method uses a deep convolutional neural network with traditional classification loss. The major strengths of our work lie in: (i) the efficient usage of synthetic data to pre-train a deep network, (ii) an adapted version of ResNet-34 architecture with region of interest pooling (referred as HWNet v2) which learns discriminative features with variable sized word images, and (iii) realistic augmentation of training data with multiple scales and elastic distortion which mimics the natural process of handwriting. We further investigate the process of fine-tuning at various layers to reduce the domain gap between synthetic and real domain and also analyze the in-variances learned at different layers using recent visualization techniques proposed in literature. #R##N#Our representation leads to state of the art word spotting performance on standard handwritten datasets and historical manuscripts in different languages with minimal representation size. On the challenging IAM dataset, our method is first to report an mAP above 0.90 for word spotting with a representation size of just 32 dimensions. Further more, we also present results on printed document datasets in English and Indic scripts which validates the generic nature of the proposed framework for learning word image representation.

Connected through hyperedge HE-1 (induced by source paper "annotation free learning of deep representations for word spotting using synthetic data and self labeling"):
  with members:
    - "exploring confidence measures for word spotting in heterogeneous datasets"
    - "attribute cnns for word spotting in handwritten documents"
    - "learning deep representations for word spotting under weak supervision"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"

Connected through hyperedge HE-2 (induced by source paper "unsupervised writer adaptation for synthetic to real handwritten word recognition"):
  with members:
    - "evaluating sequence to sequence models for handwritten text recognition"
    - "handwriting recognition in low resource scripts using adversarial learning"
    - "boosting handwriting text recognition in small databases with transfer learning"
    - "adversarial discriminative domain adaptation"
    - "zero shot deep domain adaptation"
    - "learning deep representations for word spotting under weak supervision"
    - "generating synthetic data for text recognition"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"

Connected through hyperedge HE-3 (induced by source paper "a cost efficient approach to correct ocr errors in large document collections"):
  with members:
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "ocr post processing error correction algorithm using google online spelling suggestion"
    - "ocr context sensitive error correction based on google web 1t 5 gram data set"

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)