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: "enhancing energy minimization framework for scene text recognition with top down cues"
  Abstract: An energy minimization based approach for scene text recognition with seamless integration of multiple cues.Applied also to the challenging open vocabulary setting, where a word-specific lexicon is unavailable.Comprehensive experimental evaluation on several state-of-the-art benchmarks. Recognizing scene text is a challenging problem, even more so than the recognition of scanned documents. This problem has gained significant attention from the computer vision community in recent years, and several methods based on energy minimization frameworks and deep learning approaches have been proposed. In this work, we focus on the energy minimization framework and propose a model that exploits both bottom-up and top-down cues for recognizing cropped words extracted from street images. The bottom-up cues are derived from individual character detections from an image. We build a conditional random field model on these detections to jointly model the strength of the detections and the interactions between them. These interactions are top-down cues obtained from a lexicon-based prior, i.e., language statistics. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We evaluate our proposed algorithm extensively on a number of cropped scene text benchmark datasets, namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word, and show better performance than comparable methods. We perform a rigorous analysis of all the steps in our approach and analyze the results. We also show that state-of-the-art convolutional neural network features can be integrated in our framework to further improve the recognition performance.

Connected through hyperedge HE-1 (induced by source paper "a pooling based scene text proposal technique for scene text reading in the wild"):
  with members:
    - "an end to end textspotter with explicit alignment and attention"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "wordsup exploiting word annotations for character based text detection"
    - "wordfence text detection in natural images with border awareness"
    - "icdar2017 competition on reading chinese text in the wild rctw 17"
    - "wetext scene text detection under weak supervision"
    - "deep direct regression for multi oriented scene text detection"
    - "r2cnn rotational region cnn for orientation robust scene text detection"

Connected through hyperedge HE-2 (induced by source paper "2d ctc for scene text recognition"):
  with members:
    - "scene text recognition from two dimensional perspective"
    - "an end to end textspotter with explicit alignment and attention"
    - "edit probability for scene text recognition"
    - "scan sliding convolutional attention network for scene text recognition"
    - "esir end to end scene text recognition via iterative image rectification"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "visual attention models for scene text recognition"
    - "aon towards arbitrarily oriented text recognition"

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)