human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "Embodied Question Answering"
  Abstract: We present a new AI task - Embodied Question Answering (EmbodiedQA) - where an agent is spawned at a random location in a 3D environment and asked a question ('What color is the car?'). In order to answer, the agent must first intelligently navigate to explore the environment, gather necessary visual information through first-person (egocentric) vision, and then answer the question ('orange'). EmbodiedQA requires a range of AI skills - language understanding, visual recognition, active perception, goal-driven navigation, commonsense reasoning, long-term memory, and grounding language into actions. In this work, we develop a dataset of questions and answers in House3D environments [1], evaluation metrics, and a hierarchical model trained with imitation and reinforcement learning.

Connected through hyperedge HE-1 (induced by source paper "Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering"):
  with members:
    - "Revisiting Visual Question Answering Baselines"

Connected through hyperedge HE-2 (induced by source paper "Visual Curiosity: Learning to Ask Questions to Learn Visual Recognition."):
  with members:
    (none)

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science