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 "answering visual what if questions from actions to predicted scene descriptions"):
  Abstract: In-depth scene descriptions and question answering tasks have greatly increased the scope of today's definition of scene understanding. While such tasks are in principle open ended, current formulations primarily focus on describing only the current state of the scenes under consideration. In contrast, in this paper, we focus on the future states of the scenes which are also conditioned on actions. We posit this as a question answering task, where an answer has to be given about a future scene state, given observations of the current scene, and a question that includes a hypothetical action. Our solution is a hybrid model which integrates a physics engine into a question answering architecture in order to anticipate future scene states resulting from object-object interactions caused by an action. We demonstrate first results on this challenging new problem and compare to baselines, where we outperform fully data-driven end-to-end learning approaches.

Includes members:
  Member "building generalizable agents with a realistic and rich 3d environment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "dvqa understanding data visualizations via question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "manymodalqa modality disambiguation and qa over diverse inputs")
  Member "visual interaction networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "don t just assume look and answer overcoming priors for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unbiased scene graph generation from biased training")
  Member "a simple neural network module for relational reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "learning to reason end to end module networks for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world")
  Member "airsim high fidelity visual and physical simulation for autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "taking visual motion prediction to new heightfields":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "estimating mass distribution of articulated objects through non prehensile manipulation")

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)