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 "visual question answering as a meta learning task"):
  Abstract: The predominant approach to Visual Question Answering (VQA) demands that the model represents within its weights all of the information required to answer any question about any image. Learning this information from any real training set seems unlikely, and representing it in a reasonable number of weights doubly so. We propose instead to approach VQA as a meta learning task, thus separating the question answering method from the information required. At test time, the method is provided with a support set of example questions/answers, over which it reasons to resolve the given question. The support set is not fixed and can be extended without retraining, thereby expanding the capabilities of the model. To exploit this dynamically provided information, we adapt a state-of-the-art VQA model with two techniques from the recent meta learning literature, namely prototypical networks and meta networks. Experiments demonstrate the capability of the system to learn to produce completely novel answers (i.e. never seen during training) from examples provided at test time. In comparison to the existing state of the art, the proposed method produces qualitatively distinct results with higher recall of rare answers, and a better sample efficiency that allows training with little initial data. More importantly, it represents an important step towards vision-and-language methods that can learn and reason on-the-fly.

Includes members:
  Member "a systematic study of the class imbalance problem in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "an empirical evaluation of visual question answering for novel objects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unshuffling data for improved generalization")
  Member "show ask attend and answer a strong baseline for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cq vqa visual question answering on categorized questions")
  Member "c vqa a compositional split of the visual question answering vqa v1 0 dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "challenges and prospects in vision and language research")
  Member "lifelong learning with dynamically expandable networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "prototypical networks for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "learning to remember rare events":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to compare relation semantic alignment for few shot learning")
  Member "ask the right questions active question reformulation with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "paraphrase generation with latent bag of words")

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)