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: "script identification in natural scene image and video frames using an attention based convolutional lstm network"
  Abstract: Abstract   Script identification plays a significant role in analysing documents and videos. In this paper, we focus on the problem of script identification in scene text images and video scripts. Because of low image quality, complex background and similar layout of characters shared by some scripts like Greek, Latin, etc., text recognition in those cases become challenging. In this paper, we propose a novel method that involves extraction of local and global features using CNN-LSTM framework and weighting them dynamically for script identification. First, we convert the images into patches and feed them into a CNN-LSTM framework. Attention-based patch weights are calculated applying softmax layer after LSTM. Next, we do patch-wise multiplication of these weights with corresponding CNN to yield local features. Global features are also extracted from last cell state of LSTM. We employ a fusion technique which dynamically weights the local and global features for an individual patch. Experiments have been done in four public script identification datasets: SIW-13, CVSI2015, ICDAR-17 and MLe2e. The proposed framework achieves superior results in comparison to conventional methods.

Connected through hyperedge HE-1 (induced by source paper "a deep one shot network for query based logo retrieval"):
  with members:
    - "handwriting recognition in low resource scripts using adversarial learning"
    - "texture synthesis guided deep hashing for texture image retrieval"
    - "multi scale location aware kernel representation for object detection"
    - "open logo detection challenge"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "scalable deep learning logo detection"
    - "focal loss for dense object detection"
    - "deep learning for logo recognition"

Connected through hyperedge HE-2 (induced by source paper "patch aggregator for scene text script identification"):
  with members:
    - "e2e mlt an unconstrained end to end method for multi language scene text"
    - "sequence to label script identification for multilingual ocr"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "a fine grained approach to scene text script identification"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "feature extraction and classification based on spatial spectral convlstm neural network for hyperspectral images"):
  with members:
    - "dynamic routing between capsules"
    - "bidirectional convolutional lstm based spectral spatial feature learning for hyperspectral image classification"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "training very deep networks"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "sequence to sequence learning with neural networks"
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"

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)