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 "a better way to attend attention with trees for video question answering"):
  Abstract: We propose a new attention model for video question answering. The main idea of the attention models is to locate on the most informative parts of the visual data. The attention mechanisms are quite popular these days. However, most existing visual attention mechanisms regard the question as a whole. They ignore the word-level semantics where each word can have different attentions and some words need no attention. Neither do they consider the semantic structure of the sentences. Although the extended soft attention model for video question answering leverages the word-level attention, it performs poorly on long question sentences. In this paper, we propose the heterogeneous tree-structured memory network (HTreeMN) for video question answering. Our proposed approach is based upon the syntax parse trees of the question sentences. The HTreeMN treats the words differently where the  visual  words are processed with an attention module and the  verbal  ones not. It also utilizes the semantic structure of the sentences by combining the neighbors based on the recursive structure of the parse trees. The understandings of the words and the videos are propagated and merged from leaves to the root. Furthermore, we build a hierarchical attention mechanism to distill the attended features. We evaluate our approach on two data sets. The experimental results show the superiority of our HTreeMN model over the other attention models, especially on complex questions.

Includes members:
  Member "tree structured reinforcement learning for sequential object localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pixelrl fully convolutional network with reinforcement learning for image processing")
  Member "tree to sequence attentional neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "enhancing and combining sequential and tree lstm for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "zero shot cross lingual transfer with meta learning")
  Member "image captioning with semantic attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "a focused dynamic attention model for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multimodal unified attention networks for vision and language interactions")
  Member "enhanced lstm for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial filters of dataset biases")
  Member "video fill in the blank with merging lstms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "open ended long form video question answering via hierarchical convolutional self attention networks")
  Member "hierarchical question image co attention for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

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.CV(Computer Vision and Pattern Recognition)