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 "learning to respond with stickers a framework of unifying multi modality in multi turn dialog"):
  Abstract: Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances. However, due to their large quantities, it is impractical to require text labels for the all stickers. Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context history without any external labels. Two main challenges are confronted in this task. One is to learn semantic meaning of stickers without corresponding text labels. Another challenge is to jointly model the candidate sticker with the multi-turn dialog context. To tackle these challenges, we propose a sticker response selector (SRS) model. Specifically, SRS first employs a convolutional based sticker image encoder and a self-attention based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker with each utterance in the dialog history. SRS then learns the short-term and long-term dependency between all interaction results by a fusion network to output the the final matching score. To evaluate our proposed method, we collect a large-scale real-world dialog dataset with stickers from one of the most popular online chatting platform. Extensive experiments conducted on this dataset show that our model achieves the state-of-the-art performance for all commonly-used metrics. Experiments also verify the effectiveness of each component of SRS. To facilitate further research in sticker selection field, we release this dataset of 340K multi-turn dialog and sticker pairs.

Includes members:
  Member "image question answer synergistic network for visual dialog":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modality balanced models for visual dialogue")
  Member "understanding chat messages for sticker recommendation in hike messenger":
    also connected through other hyperedges:
      (none)
  Member "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving multi turn response selection models with complementary last utterance selection by instance weighting")
  Member "product aware answer generation in e commerce question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "abstractive text summarization by incorporating reader comments")
  Member "how to write summaries with patterns learning towards abstractive summarization through prototype editing":
    also connected through other hyperedges:
      (none)
  Member "abstractive text summarization by incorporating reader comments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "how to write summaries with patterns learning towards abstractive summarization through prototype editing")
  Member "think visually question answering through virtual imagery":
    also connected through other hyperedges:
      (none)
  Member "multimodal emoji prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding chat messages for sticker recommendation in hike messenger")

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)