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 "understand compose and respond answering visual questions by a composition of abstract procedures"):
  Abstract: An image related question defines a specific visual task that is required in order to produce an appropriate answer. The answer may depend on a minor detail in the image and require complex reasoning and use of prior knowledge. When humans perform this task, they are able to do it in a flexible and robust manner, integrating modularly any novel visual capability with diverse options for various elaborations of the task. In contrast, current approaches to solve this problem by a machine are based on casting the problem as an end-to-end learning problem, which lacks such abilities. #R##N#We present a different approach, inspired by the aforementioned human capabilities. The approach is based on the compositional structure of the question. The underlying idea is that a question has an abstract representation based on its structure, which is compositional in nature. The question can consequently be answered by a composition of procedures corresponding to its substructures. The basic elements of the representation are logical patterns, which are put together to represent the question. These patterns include a parametric representation for object classes, properties and relations. Each basic pattern is mapped into a basic procedure that includes meaningful visual tasks, and the patterns are composed to produce the overall answering procedure. #R##N#The UnCoRd (Understand Compose and Respond) system, based on this approach, integrates existing detection and classification schemes for a set of object classes, properties and relations. These schemes are incorporated in a modular manner, providing elaborated answers and corrections for negative answers. In addition, an external knowledge base is queried for required common-knowledge. We performed a qualitative analysis of the system, which demonstrates its representation capabilities and provide suggestions for future developments.

Includes members:
  Member "tell and answer towards explainable visual question answering using attributes and captions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "clevrer collision events for video representation and reasoning")
  Member "vqa e explaining elaborating and enhancing your answers for visual questions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "set structured latent representations")
  Member "improved fusion of visual and language representations by dense symmetric co attention for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks")
  Member "inferring and executing programs for visual reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised question decomposition for question answering")
  Member "visual question answering datasets algorithms and future challenges":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a question centric model for visual question answering in medical imaging")
  Member "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "interactive natural language based person search")

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)