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: "guided feature transformation gft a neural language grounding module for embodied agents"
  Abstract: Recently there has been a rising interest in training agents, embodied in virtual environments, to perform language-directed tasks by deep reinforcement learning. In this paper, we propose a simple but effective neural language grounding module for embodied agents that can be trained end to end from scratch taking raw pixels, unstructured linguistic commands, and sparse rewards as the inputs. We model the language grounding process as a language-guided transformation of visual features, where latent sentence embeddings are used as the transformation matrices. In several language-directed navigation tasks that feature challenging partial observability and require simple reasoning, our module significantly outperforms the state of the art. We also release XWorld3D, an easy-to-customize 3D environment that can potentially be modified to evaluate a variety of embodied agents.

Connected through hyperedge HE-1 (induced by source paper "self monitoring navigation agent via auxiliary progress estimation"):
  with members:
    - "neural baby talk"
    - "talk the walk navigating new york city through grounded dialogue"
    - "gibson env real world perception for embodied agents"
    - "speaker follower models for vision and language navigation"
    - "on evaluation of embodied navigation agents"
    - "end to end dense video captioning with masked transformer"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "omnidirectional cnn for visual place recognition and navigation"

Connected through hyperedge HE-2 (induced by source paper "embodied multimodal multitask learning"):
  with members:
    - "building generalizable agents with a realistic and rich 3d environment"
    - "compositional attention networks for machine reasoning"
    - "the sound of pixels"
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "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-3 (induced by source paper "sim real joint reinforcement transfer for 3d indoor navigation"):
  with members:
    - "dm gan dynamic memory generative adversarial networks for text to image synthesis"
    - "video to video synthesis"
    - "adversarial learning with local coordinate coding"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "chalet cornell house agent learning environment"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "unsupervised reinforcement learning of transferable meta skills for embodied navigation"):
  with members:
    - "on first order meta learning algorithms"
    - "unsupervised meta learning for reinforcement learning"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "on evaluation of embodied navigation agents"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "diversity is all you need learning skills without a reward function"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"

Connected through hyperedge HE-5 (induced by source paper "the regretful agent heuristic aided navigation through progress estimation"):
  with members:
    - "self monitoring navigation agent via auxiliary 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"
    - "gibson env real world perception for embodied agents"
    - "speaker follower models for vision and language navigation"
    - "on evaluation of embodied navigation agents"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "task oriented language grounding for language input with multiple sub goals of non linear order"):
  with members:
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "a deep compositional framework for human like language acquisition in virtual environment"
    - "hindsight experience replay"
    - "gated attention architectures for task oriented language grounding"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "prioritized experience replay"
    - "listen attend and walk neural mapping of navigational instructions to action sequences"

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.AI(Artificial Intelligence)