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 "vqa with no questions answers training"):
  Abstract: Methods for teaching machines to answer visual questions have made significant progress in the last few years, but although demonstrating impressive results on particular datasets, these methods lack some important human capabilities, including integrating new visual classes and concepts in a modular manner, providing explanations for the answer and handling new domains without new examples. In this paper we present a system that achieves state-of-the-art results on the CLEVR dataset without any questions-answers training, utilizes real visual estimators and explains the answer. The system includes a question representation stage followed by an answering procedure, which invokes an extendable set of visual estimators. It can explain the answer, including its failures, and provide alternatives to negative answers. The scheme builds upon a framework proposed recently, with extensions allowing the system to deal with novel domains without relying on training examples.

Includes members:
  Member "neural symbolic vqa disentangling reasoning from vision and language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep adaptive semantic logic dasl compiling declarative knowledge into deep neural networks")
  Member "tell and answer towards explainable visual question answering using attributes and captions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relation aware graph attention network for visual question answering")
  Member "explicit reasoning over end to end neural architectures for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "clevrer collision events for video representation and reasoning")
  Member "understand compose and respond answering visual questions by a composition of abstract procedures":
    also connected through other hyperedges:
      (none)
  Member "vqa e explaining elaborating and enhancing your answers for visual questions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "challenges and prospects in vision and language research")
  Member "object based reasoning in vqa":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "set structured latent representations")
  Member "vizwiz grand challenge answering visual questions from blind people":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "in defense of grid features for visual question answering")
  Member "improved fusion of visual and language representations by dense symmetric co attention for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks")

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)