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: "ultra power efficient cnn domain specific accelerator with 9 3tops watt for mobile and embedded applications"
  Abstract: Computer vision performances have been significantly improved in recent years by Convolutional Neural Networks(CNN). Currently, applications using CNN algorithms are deployed mainly on general purpose hardwares, such as CPUs, GPUs or FPGAs. However, power consumption, speed, accuracy, memory footprint, and die size should all be taken into consideration for mobile and embedded applications. Domain Specific Architecture (DSA) for CNN is the efficient and practical solution for CNN deployment and implementation. We designed and produced a 28nm Two-Dimensional CNN-DSA accelerator with an ultra power-efficient performance of 9.3TOPS/Watt and with all processing done in the internal memory instead of outside DRAM. It classifies 224x224 RGB image inputs at more than 140fps with peak power consumption at less than 300mW and an accuracy comparable to the VGG benchmark. The CNN-DSA accelerator is reconfigurable to support CNN model coefficients of various layer sizes and layer types, including convolution, depth-wise convolution, short-cut connections, max pooling, and ReLU. Furthermore, in order to better support real-world deployment for various application scenarios, especially with low-end mobile and embedded platforms and MCUs (Microcontroller Units), we also designed algorithms to fully utilize the CNN-DSA accelerator efficiently by reducing the dependency on external accelerator computation resources, including implementation of Fully-Connected (FC) layers within the accelerator and compression of extracted features from the CNN-DSA accelerator. Live demos with our CNN-DSA accelerator on mobile and embedded systems show its capabilities to be widely and practically applied in the real world.

Connected through hyperedge HE-1 (induced by source paper "supertml two dimensional word embedding for the precognition on structured tabular data"):
  with members:
    - "squared english word a method of generating glyph to use super characters for sentiment analysis"
    - "neural feature learning from relational database"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "super characters a conversion from sentiment classification to image classification"
    - "catboost unbiased boosting with categorical features"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-2 (induced by source paper "supercaptioning image captioning using two dimensional word embedding"):
  with members:
    - "superchat dialogue generation by transfer learning from vision to language using two dimensional word embedding"
    - "supertml two dimensional word embedding and transfer learning using imagenet pretrained cnn models for the classifications on tabular data"
    - "supertml two dimensional word embedding for the precognition on structured tabular data"
    - "squared english word a method of generating glyph to use super characters for sentiment analysis"
    - "a comprehensive survey of deep learning for image captioning"
    - "super characters a conversion from sentiment classification to image classification"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (induced by source paper "superchat dialogue generation by transfer learning from vision to language using two dimensional word embedding"):
  with members:
    - "supertml two dimensional word embedding and transfer learning using imagenet pretrained cnn models for the classifications on tabular data"
    - "squared english word a method of generating glyph to use super characters for sentiment analysis"
    - "generating multiple diverse responses for short text conversation"
    - "super characters a conversion from sentiment classification to image classification"
    - "attention is all you need"
    - "beam search strategies for neural machine translation"
    - "squeeze and excitation networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-4 (induced by source paper "system demo for transfer learning across vision and text using domain specific cnn accelerator for on device nlp applications"):
  with members:
    - "superchat dialogue generation by transfer learning from vision to language using two dimensional word embedding"
    - "supertml two dimensional word embedding for the precognition on structured tabular data"
    - "supercaptioning image captioning using two dimensional word embedding"
    - "squared english word a method of generating glyph to use super characters for sentiment analysis"
    - "super characters a conversion from sentiment classification to image classification"
    - "which encoding is the best for text classification in chinese english japanese and korean"
    - "character level convolutional networks for text classification"
    - "very deep convolutional networks for large scale image 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)