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: "confidence prediction for lexicon free ocr"
  Abstract: Having a reliable accuracy score is crucial for real world applications of OCR, since such systems are judged by the number of false readings. Lexicon-based OCR systems, which deal with what is essentially a multi-class classification problem, often employ methods explicitly taking into account the lexicon, in order to improve accuracy. However, in lexicon-free scenarios, filtering errors requires an explicit confidence calculation. In this work we show two explicit confidence measurement techniques, and show that they are able to achieve a significant reduction in misreads on both standard benchmarks and a proprietary dataset.

Connected through hyperedge HE-1 (induced by source paper "candidate fusion integrating language modelling into a sequence to sequence handwritten word recognition architecture"):
  with members:
    - "are 2d lstm really dead for offline text recognition"
    - "attention is all you need"
    - "carla an open urban driving simulator"
    - "generating synthetic data for text recognition"
    - "a critical review of recurrent neural networks for sequence learning"
    - "end to end attention based large vocabulary speech recognition"
    - "attention based models for speech recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (induced by source paper "deep pnml predictive normalized maximum likelihood for deep neural networks"):
  with members:
    - "a new look at an old problem a universal learning approach to linear regression"
    - "universal supervised learning for individual data"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "handwriting recognition in low resource scripts using adversarial learning"):
  with members:
    - "vital visual tracking via adversarial learning"
    - "cross language framework for word recognition and spotting of indic scripts"
    - "synthetic data generation for indic handwritten text recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "adversarial discriminative domain adaptation"
    - "attribute cnns for word spotting in handwritten documents"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)