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 "videonavqa bridging the gap between visual and embodied question answering"):
  Abstract: Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely on its egocentric input to answer a given question. The desired outcome is that the agent learns to combine capabilities such as scene understanding, navigation and language understanding in order to perform complex reasoning in the visual world. However, initial advancements combining standard vision and language methods with imitation and reinforcement learning algorithms have shown EQA might be too complex and challenging for these techniques. In order to investigate the feasibility of EQA-type tasks, we build the VideoNavQA dataset that contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task. We investigate several models, adapted from popular VQA methods, on this new benchmark. This establishes an initial understanding of how well VQA-style methods can perform within this novel EQA paradigm.

Includes members:
  Member "embodied question answering in photorealistic environments with point cloud perception":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin")
  Member "multi target embodied question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin")
  Member "benchmarking classic and learned navigation in complex 3d environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an exploration of embodied visual exploration")
  Member "gqa a new dataset for real world visual reasoning and compositional question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unbiased scene graph generation from biased training")
  Member "habitat a platform for embodied ai research":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "why build an assistant in minecraft")
  Member "murel multimodal relational reasoning for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "learning visual question answering by bootstrapping hard attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "systematic generalization what is required and can it be learned":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration")

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)