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: "hmm based indic handwritten word recognition using zone segmentation"
  Abstract: This paper presents a novel approach towards Indic handwritten word recognition using zone-wise information. Because of complex nature due to compound characters, modifiers, overlapping and touching, etc., character segmentation and recognition is a tedious job in Indic scripts (e.g. Devanagari, Bangla, Gurumukhi, and other similar scripts). To avoid character segmentation in such scripts, HMM-based sequence modeling has been used earlier in holistic way. This paper proposes an efficient word recognition framework by segmenting the handwritten word images horizontally into three zones (upper, middle and lower) and then recognize the corresponding zones. The main aim of this zone segmentation approach is to reduce the number of distinct component classes compared to the total number of classes in Indic scripts. As a result, use of this zone segmentation approach enhances the recognition performance of the system. The components in middle zone, where characters are mostly touching, are recognized using HMM. After the recognition of middle zone, HMM based Viterbi forced alignment is applied to mark the left and right boundaries of the characters in the middle zone. Next, the residue components, if any, in upper and lower zones are obtained in a character boundary then the components are combined with the character to achieve the final word level recognition. Water reservoir-based properties have been integrated in this framework to improve the zone segmentation and character boundary detection defects while segmentation. A novel sliding window-based feature, called Pyramid Histogram of Oriented Gradient (PHOG) is proposed for middle zone recognition. PHOG features have been compared with other existing features and found robust for Indic script recognition. An exhaustive experiment is performed on two Indic scripts namely, Bangla and Devanagari for the performance evaluation. From the experiment, it has been noted that proposed zone-wise recognition improves accuracy with respect to the traditional way of Indic word recognition. A novel approach of Indic handwritten word recognition using zone segmentation.Efficient PHOG features developed to improve the performance of HMM based middle zone recognition.Integration of water reservoir concept for better character alignment in a word image.A detailed study of experimental results in Bangla and Devanagari scripts has been performed.The proposed framework outperforms traditional without-zone-segmentation based recognition systems.

Connected through hyperedge HE-1 (induced by source paper "handwriting recognition in low resource scripts using adversarial learning"):
  with members:
    - "confidence prediction for lexicon free ocr"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "indic handwritten script identification using offline online multimodal deep network"):
  with members:
    - "deep learning for word level handwritten indic script identification"
    - "handwriting trajectory recovery using end to end deep encoder decoder network"
    - "cross language framework for word recognition and spotting of indic scripts"
    - "hmm based writer identification in music score documents without staff line removal"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "local binary pattern for word spotting in handwritten historical document"
    - "compact bilinear pooling"

Connected through hyperedge HE-3 (induced by source paper "word searching in scene image and video frame in multi script scenario using dynamic shape coding"):
  with members:
    - "date field retrieval in scene image and video frames using text enhancement and shape coding"
    - "multi oriented text detection and verification in video frames and scene images"
    - "text recognition in scene image and video frame using color channel selection"
    - "cross language framework for word recognition and spotting of indic scripts"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "reading scene text in deep convolutional sequences"
    - "reading text in the wild with convolutional neural 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)