human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "understanding grounded language learning 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 even execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome certain well-studied learning challenges that are also fundamental to infants learning their first words. While it is notable that models with no meaningful prior knowledge overcome these learning obstacles, AI researchers and practitioners currently lack a clear understanding of exactly how they do so. Here we address this question as a way of achieving a clearer general understanding of grounded language learning, both to inform future research and to improve confidence in model predictions. For maximum control and generality, we focus on a simple neural network-based language learning agent trained via policy-gradient methods to interpret synthetic linguistic instructions in a simulated 3D world. We apply experimental paradigms from developmental psychology to this agent, exploring the conditions under which established human biases and learning effects emerge. We further propose a novel way to visualise and analyse semantic representation in grounded language learning agents that yields a plausible computational account of the observed effects.

Connected through hyperedge HE-1 (induced by source paper "retouchdown adding touchdown to streetlearn as a shareable resource for language grounding tasks in street view"):
  with members:
    - "navigation agents for the visually impaired a sidewalk simulator and experiments"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "valan vision and language agent navigation"
    - "effective and general evaluation for instruction conditioned navigation using dynamic time warping"
    - "the streetlearn environment and dataset"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"

Connected through hyperedge HE-2 (induced by source paper "environmental drivers of systematicity and generalization in a situated agent"):
  with members:
    - "human few shot learning of compositional instructions"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "compositional generalization through meta sequence to sequence learning"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "systematic generalization what is required and can it be learned"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-3 (induced by source paper "learning to follow directions in street view"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning visual question answering by bootstrapping hard attention"
    - "global pose estimation with an attention based recurrent network"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"

Connected through hyperedge HE-4 (induced by source paper "talk2nav long range vision and language navigation with dual attention and spatial memory"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "learning accurate comfortable and human like driving"
    - "learning to follow directions in street view"
    - "grounding human to vehicle advice for self driving vehicles"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "talk the walk navigating new york city through grounded dialogue"

Connected through hyperedge HE-5 (induced by source paper "two body problem collaborative visual task completion"):
  with members:
    - "tarmac targeted multi agent communication"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "neural modular control for embodied question answering"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "unifying map and landmark based representations for visual navigation"
    - "home a household multimodal environment"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "stabilising experience replay for deep multi agent reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "emergent systematic generalization in a situated agent"):
  with members:
    - "human few shot learning of compositional instructions"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "compositional generalization through meta sequence to sequence learning"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "systematic generalization what is required and can it be learned"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-7 (induced by source paper "emergence of numeric concepts in multi agent autonomous communication"):
  with members:
    - "measuring compositionality in representation learning"
    - "on the pitfalls of measuring emergent communication"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "emergent communication through negotiation"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-8 (induced by source paper "learning to navigate in cities without a map"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "semi parametric topological memory for navigation"
    - "a critical investigation of deep reinforcement learning for navigation"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "active neural localization"
    - "the adobeindoornav dataset towards deep reinforcement learning based real world indoor robot visual navigation"
    - "unsupervised predictive memory in a goal directed agent"
    - "automated curriculum learning for neural networks"

Question: Please tell me which class the center node 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)