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: "interactive grounded language acquisition and generalization in a 2d world"
  Abstract: We build a virtual agent for learning language in a 2D maze-like world. The agent sees images of the surrounding environment, listens to a virtual teacher, and takes actions to receive rewards. It interactively learns the teacher's language from scratch based on two language use cases: sentence-directed navigation and question answering. It learns simultaneously the visual representations of the world, the language, and the action control. By disentangling language grounding from other computational routines and sharing a concept detection function between language grounding and prediction, the agent reliably interpolates and extrapolates to interpret sentences that contain new word combinations or new words missing from training sentences. The new words are transferred from the answers of language prediction. Such a language ability is trained and evaluated on a population of over 1.6 million distinct sentences consisting of 119 object words, 8 color words, 9 spatial-relation words, and 50 grammatical words. The proposed model significantly outperforms five comparison methods for interpreting zero-shot sentences. In addition, we demonstrate human-interpretable intermediate outputs of the model in the appendix.

Connected through hyperedge HE-1 (induced by source paper "exploiting language instructions for interpretable and compositional reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "learning to understand goal specifications by modelling reward"
    - "reverse curriculum generation for reinforcement learning"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"
    - "visualizing and understanding atari agents"

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"
    - "understanding grounded language learning agents"

Connected through hyperedge HE-3 (induced by source paper "why build an assistant in minecraft"):
  with members:
    - "craftassist a framework for dialogue enabled interactive agents"
    - "habitat a platform for embodied ai research"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "towards deep conversational recommendations"
    - "learning to ask questions in open domain conversational systems with typed decoders"

Connected through hyperedge HE-4 (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"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "embodied multimodal multitask learning"):
  with members:
    - "guided feature transformation gft a neural language grounding module for embodied agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "compositional attention networks for machine reasoning"
    - "the sound of pixels"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "gated attention architectures for task oriented language grounding"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "aligned image word representations improve inductive transfer across vision language tasks"

Connected through hyperedge HE-6 (induced by source paper "rerere remote embodied referring expressions in real indoor environments"):
  with members:
    - "grounding visual explanations"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "gibson env real world perception for embodied agents"
    - "speaker follower models for vision and language navigation"
    - "mattnet modular attention network for referring expression comprehension"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "learning to navigate in cities without a map"

Connected through hyperedge HE-7 (induced by source paper "look listen and act towards audio visual embodied navigation"):
  with members:
    - "self supervised moving vehicle tracking with stereo sound"
    - "the sound of motions"
    - "self supervised audio visual co segmentation"
    - "audio visual embodied navigation"
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "semi parametric topological memory for navigation"

Connected through hyperedge HE-8 (induced by source paper "mastering emergent language learning to guide in simulated navigation"):
  with members:
    - "on the pitfalls of measuring emergent communication"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "learning to understand goal specifications by modelling reward"
    - "guiding policies with language via meta learning"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "home a household multimodal environment"

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)