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: "learning deep representations for word spotting under weak supervision"
  Abstract: Convolutional Neural Networks have made their mark in various fields of computer vision in recent years. They have achieved state-of-the-art performance in the field of document analysis as well. However, CNNs require a large amount of annotated training data and, hence, great manual effort. In our approach, we introduce a method to drastically reduce the manual annotation effort while retaining the high performance of a CNN for word spotting in handwritten documents. The model is learned with weak supervision using a combination of synthetically generated training data and a small subset of the training partition of the handwritten data set. We show that the network achieves results highly competitive to the state-of-the-art in word spotting with shorter training times and a fraction of the annotation effort.

Connected through hyperedge HE-1 (induced by source paper "fine grained image classification and retrieval by combining visual and locally pooled textual features"):
  with members:
    - "towards vqa models that can read"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "on the variance of the adaptive learning rate and beyond"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "lookahead optimizer k steps forward 1 step back"
    - "single shot scene text retrieval"
    - "an end to end textspotter with explicit alignment and attention"
    - "learning to navigate for fine grained classification"

Connected through hyperedge HE-2 (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"
    - "hwnet v2 an efficient word image representation for handwritten documents"
    - "attribute cnns for word spotting in handwritten documents"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"

Connected through hyperedge HE-3 (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"
    - "hwnet v2 an efficient word image representation for handwritten documents"
    - "boosting handwriting text recognition in small databases with transfer learning"
    - "adversarial discriminative domain adaptation"
    - "zero shot deep domain adaptation"
    - "generating synthetic data for text recognition"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"

Connected through hyperedge HE-4 (induced by source paper "lifelong learning for text retrieval and recognition in historical handwritten document collections"):
  with members:
    - "a large scale field test on word image classification in large historical document collections using a traditional and two deep learning methods"
    - "open set chinese character recognition using multi typed attributes"
    - "joint line segmentation and transcription for end to end handwritten paragraph recognition"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-5 (induced by source paper "exploring confidence measures for word spotting in heterogeneous datasets"):
  with members:
    - "principled uncertainty estimation for deep neural networks"
    - "attribute cnns for word spotting in handwritten documents"
    - "matching handwritten document images"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-6 (induced by source paper "attribute cnns for word spotting in handwritten documents"):
  with members:
    - "information theoretical label embeddings for large scale image classification"
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "matching handwritten document images"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

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)