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 "understanding early word learning in situated artificial agents"):
  Abstract: Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome challenges that infants face when learning their first words. While it is notable that models with no meaningful prior knowledge overcome these obstacles, researchers currently lack a clear understanding of how they do so, a problem that we attempt to address in this paper. For maximum control and generality, we focus on a simple neural network-based language learning agent, trained via policy-gradient methods, which can interpret single-word instructions in a simulated 3D world. Whilst the goal is not to explicitly model infant word learning, we take inspiration from experimental paradigms in developmental psychology and apply some of these to the artificial agent, exploring the conditions under which established human biases and learning effects emerge. We further propose a novel method for visualising semantic representations in the agent.

Includes members:
  Member "mapping instructions to actions in 3d environments with visual goal prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "retouchdown adding touchdown to streetlearn as a shareable resource for language grounding tasks in street view")
  Member "learning to understand goal specifications by modelling reward":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration")
  Member "neural modular control for embodied question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an exploration of embodied visual exploration")
  Member "zero shot task generalization with multi task deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on catastrophic interference in atari 2600 games")
  Member "mapping instructions and visual observations to actions with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards learning a generic agent for vision and language navigation via pre training")
  Member "grounded language learning in a simulated 3d world":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exploiting language instructions for interpretable and compositional reinforcement learning")
  Member "visual analysis of hidden state dynamics in recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the what if tool interactive probing of machine learning models")
  Member "dynamic memory networks for visual and textual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "component analysis for visual question answering architectures")

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.CL(Computation and Language)