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 "constructing hierarchical q a datasets for video story understanding"):
  Abstract: Video understanding is emerging as a new paradigm for studying human-like AI. Question-and-Answering (Q&A) is used as a general benchmark to measure the level of intelligence for video understanding. While several previous studies have suggested datasets for video Q&A tasks, they did not really incorporate story-level understanding, resulting in highly-biased and lack of variance in degree of question difficulty. In this paper, we propose a hierarchical method for building Q&A datasets, i.e. hierarchical difficulty levels. We introduce three criteria for video story understanding, i.e. memory capacity, logical complexity, and DIKW (Data-Information-Knowledge-Wisdom) pyramid. We discuss how three-dimensional map constructed from these criteria can be used as a metric for evaluating the levels of intelligence relating to video story understanding.

Includes members:
  Member "tvqa localized compositional video question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical conditional relation networks for video question answering")
  Member "tgif qa toward spatio temporal reasoning in visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "deepstory video story qa by deep embedded memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "clevr a diagnostic dataset for compositional language and elementary visual reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "marioqa answering questions by watching gameplay videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "clevrer collision events for video representation and reasoning")
  Member "visual7w grounded question answering in images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "exploring models and data for image question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "ask your neurons a neural based approach to answering questions about images":
    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.AI(Artificial Intelligence)