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: "from language to goals inverse reinforcement learning for vision based instruction following"
  Abstract: Reinforcement learning is a promising framework for solving control problems, but its use in practical situations is hampered by the fact that reward functions are often difficult to engineer. Specifying goals and tasks for autonomous machines, such as robots, is a significant challenge: conventionally, reward functions and goal states have been used to communicate objectives. But people can communicate objectives to each other simply by describing or demonstrating them. How can we build learning algorithms that will allow us to tell machines what we want them to do? In this work, we investigate the problem of grounding language commands as reward functions using inverse reinforcement learning, and argue that language-conditioned rewards are more transferable than language-conditioned policies to new environments. We propose language-conditioned reward learning (LC-RL), which grounds language commands as a reward function represented by a deep neural network. We demonstrate that our model learns rewards that transfer to novel tasks and environments on realistic, high-dimensional visual environments with natural language commands, whereas directly learning a language-conditioned policy leads to poor performance.

Connected through hyperedge HE-1 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration"):
  with members:
    - "emergent systematic generalization in a situated agent"
    - "skew fit state covering self supervised reinforcement learning"
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "monet unsupervised scene decomposition and representation"
    - "extending machine language models toward human level language understanding"
    - "using natural language for reward shaping in reinforcement learning"
    - "on the limitations of representing functions on sets"
    - "multi object representation learning with iterative variational inference"

Connected through hyperedge HE-2 (induced by source paper "deep compositional robotic planners that follow natural language commands"):
  with members:
    - "from semantics to execution integrating action planning with reinforcement learning for robotic causal problem solving"
    - "from semantics to execution integrating action planning with reinforcement learning for robotic tool use"
    - "prospection interpretable plans from language by predicting the future"
    - "learning to plan via neural exploration exploitation trees"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "deep sequential models for sampling based planning"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "gated path planning networks"

Connected through hyperedge HE-3 (induced by source paper "combating false negatives in adversarial imitation learning"):
  with members:
    - "task relevant adversarial imitation learning"
    - "positive unlabeled reward learning"
    - "a survey of reinforcement learning informed by natural language"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "learning to understand goal specifications by modelling reward"
    - "learning to navigate in cities without a map"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "gated attention architectures for task oriented language grounding"

Connected through hyperedge HE-4 (induced by source paper "spatial concept based navigation with human speech instructions via probabilistic inference on bayesian generative model"):
  with members:
    - "steap simultaneous trajectory estimation and planning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "visual reinforcement learning with imagined goals"
    - "improved and scalable online learning of spatial concepts and language models with mapping"
    - "online spatial concept and lexical acquisition with simultaneous localization and mapping"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "spatial concept acquisition for a mobile robot that integrates self localization and unsupervised word discovery from spoken sentences"
    - "symbol emergence in robotics a survey"

Connected through hyperedge HE-5 (induced by source paper "reward rational implicit choice a unifying formalism for reward learning"):
  with members:
    - "preferences implicit in the state of the world"
    - "active inverse reward design"
    - "inverse reward design"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "generative adversarial imitation learning"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "the off switch game"
    - "a joint model of language and perception for grounded attribute learning"

Connected through hyperedge HE-6 (induced by source paper "a survey of reinforcement learning informed by natural language"):
  with members:
    - "assessing bert s syntactic abilities"
    - "learning to generalize from sparse and underspecified rewards"
    - "using natural language for reward shaping in reinforcement learning"
    - "hierarchical decision making by generating and following natural language instructions"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "an algorithmic perspective on imitation learning"
    - "flipdial a generative model for two way visual dialogue"

Connected through hyperedge HE-7 (induced by source paper "language as an abstraction for hierarchical deep reinforcement learning"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "a survey of reinforcement learning informed by natural language"
    - "hierarchical reinforcement learning with hindsight"
    - "temporal difference models model free deep rl for model based control"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "visual reinforcement learning with imagined goals"
    - "bleu is not suitable for the evaluation of text simplification"

Connected through hyperedge HE-8 (induced by source paper "perceive transform and act multi modal attention networks for vision and language navigation"):
  with members:
    - "embodied vision and language navigation with dynamic convolutional filters"
    - "rerere remote embodied referring expressions in real indoor environments"
    - "embodied visual recognition"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "effective and general evaluation for instruction conditioned navigation using dynamic time warping"
    - "situational fusion of visual representation for visual navigation"
    - "stay on the path instruction fidelity in vision and language navigation"
    - "videobert a joint model for video and language representation learning"

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.LG(Machine Learning)