human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "system demo for transfer learning across vision and text using domain specific cnn accelerator for on device nlp applications"):
  Abstract: Power-efficient CNN Domain Specific Accelerator (CNN-DSA) chips are currently available for wide use in mobile devices. These chips are mainly used in computer vision applications. However, the recent work of Super Characters method for text classification and sentiment analysis tasks using two-dimensional CNN models has also achieved state-of-the-art results through the method of transfer learning from vision to text. In this paper, we implemented the text classification and sentiment analysis applications on mobile devices using CNN-DSA chips. Compact network representations using one-bit and three-bits precision for coefficients and five-bits for activations are used in the CNN-DSA chip with power consumption less than 300mW. For edge devices under memory and compute constraints, the network is further compressed by approximating the external Fully Connected (FC) layers within the CNN-DSA chip. At the workshop, we have two system demonstrations for NLP tasks. The first demo classifies the input English Wikipedia sentence into one of the 14 ontologies. The second demo classifies the Chinese online-shopping review into positive or negative.

Includes members:
  Member "superchat dialogue generation by transfer learning from vision to language using two dimensional word embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi modal sentiment analysis using super characters method on low power cnn accelerator device")
  Member "supertml two dimensional word embedding for the precognition on structured tabular data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi modal sentiment analysis using super characters method on low power cnn accelerator device")
  Member "supercaptioning image captioning using two dimensional word embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi modal sentiment analysis using super characters method on low power cnn accelerator device")
  Member "squared english word a method of generating glyph to use super characters for sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi modal sentiment analysis using super characters method on low power cnn accelerator device")
  Member "ultra power efficient cnn domain specific accelerator with 9 3tops watt for mobile and embedded applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "supertml two dimensional word embedding for the precognition on structured tabular data")
  Member "super characters a conversion from sentiment classification to image classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi modal sentiment analysis using super characters method on low power cnn accelerator device")
  Member "which encoding is the best for text classification in chinese english japanese and korean":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "character level convolutional networks for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "description based text classification with reinforcement learning")

Question: Please tell me which class the source paper 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.CL(Computation and Language)