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: "multi scale attention with dense encoder for handwritten mathematical expression recognition"
  Abstract: Handwritten mathematical expression recognition is a challenging problem due to the complicated two-dimensional structures, ambiguous handwriting input and variant scales of handwritten math symbols. To settle this problem, we utilize the attention based encoder-decoder model that recognizes mathematical expression images from two-dimensional layouts to one-dimensional LaTeX strings. We improve the encoder by employing densely connected convolutional networks as they can strengthen feature extraction and facilitate gradient propagation especially on a small training set. We also present a novel multi-scale attention model which is employed to deal with the recognition of math symbols in different scales and save the fine-grained details that will be dropped by pooling operations. Validated on the CROHME competition task, the proposed method significantly outperforms the state-of-the-art methods with an expression recognition accuracy of 52.8% on CROHME 2014 and 50.1% on CROHME 2016, by only using the official training dataset.

Connected through hyperedge HE-1 (induced by source paper "recognizing handwritten mathematical expressions as latex sequences using a multiscale robust neural network"):
  with members:
    - "modeling coverage for neural machine translation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "pattern generation strategies for improving recognition of handwritten mathematical expressions"):
  with members:
    - "image to markup generation with coarse to fine attention"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "very deep convolutional networks for large scale image recognition"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "a human inspired recognition system for premodern japanese historical documents"):
  with members:
    - "building efficient cnn architecture for offline handwritten chinese character recognition"
    - "full page text recognition learning where to start and when to stop"
    - "show attend and tell neural image caption generation with visual attention"
    - "neural machine translation by jointly learning to align and translate"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "end to end recognition system for recognizing offline unconstrained vietnamese handwriting"):
  with members:
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "densely connected convolutional networks"
    - "image to markup generation with coarse to fine attention"

Connected through hyperedge HE-5 (induced by source paper "deep learning approach for receipt recognition"):
  with members:
    - "east an efficient and accurate scene text detector"
    - "detecting text in natural image with connectionist text proposal network"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"

Connected through hyperedge HE-6 (induced by source paper "stroke extraction for offline handwritten mathematical expression recognition"):
  with members:
    - "pattern generation strategies for improving recognition of handwritten mathematical expressions"
    - "image to markup generation with coarse to fine attention"

Connected through hyperedge HE-7 (induced by source paper "trajectory based radical analysis network for online handwritten chinese character recognition"):
  with members:
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "radical analysis network for zero shot learning in printed chinese character recognition"
    - "modeling coverage for neural machine translation"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "effective approaches to attention based neural machine translation"
    - "end to end attention based large vocabulary speech recognition"
    - "show attend and tell neural image caption generation with visual attention"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"

Connected through hyperedge HE-8 (induced by source paper "denseran for offline handwritten chinese character recognition"):
  with members:
    - "radical analysis network for zero shot learning in printed chinese character recognition"
    - "densely connected convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "high performance offline handwritten chinese character recognition using googlenet and directional feature maps"
    - "natural language understanding with distributed representation"
    - "very deep convolutional networks for large scale image recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

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)