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 "an active information seeking model for goal oriented vision and language tasks"):
  Abstract: As Computer Vision algorithms move from passive analysis of pixels to active reasoning over semantics, the breadth of information algorithms need to reason over has expanded significantly. One of the key challenges in this vein is the ability to identify the information required to make a decision, and select an action that will recover this information. We propose an reinforcement-learning approach that maintains an distribution over its internal information, thus explicitly representing the ambiguity in what it knows, and needs to know, towards achieving its goal. Potential actions are then generated according to particles sampled from this distribution. For each potential action a distribution of the expected answers is calculated, and the value of the information gained is obtained, as compared to the existing internal information. We demonstrate this approach applied to two vision-language problems that have attracted significant recent interest, visual dialogue and visual query generation. In both cases the method actively selects actions that will best reduce its internal uncertainty, and outperforms its competitors in achieving the goal of the challenge.

Includes members:
  Member "compositional attention networks for machine reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unshuffling data for improved generalization")
  Member "ddrprog a clevr differentiable dynamic reasoning programmer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "giving commands to a self driving car a multimodal reasoner for visual grounding")
  Member "inferring and executing programs for visual reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised question decomposition for question answering")
  Member "improved image captioning via policy gradient optimization of spider":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "deep reinforcement learning based image captioning with embedding reward":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "a simple neural network module for relational reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "learning cooperative visual dialog agents with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "stein variational policy gradient":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "curriculum in gradient based meta reinforcement learning")

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.LG(Machine Learning)