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: "navigational instruction generation as inverse reinforcement learning with neural machine translation"
  Abstract: Modern robotics applications that involve human-robot interaction require robots to be able to communicate with humans seamlessly and effectively. Natural language provides a flexible and efficient medium through which robots can exchange information with their human partners. Significant advancements have been made in developing robots capable of interpreting free-form instructions, but less attention has been devoted to endowing robots with the ability to generate natural language. We propose a navigational guide model that enables robots to generate natural language instructions that allow humans to navigate a priori unknown environments. We first decide which information to share with the user according to their preferences, using a policy trained from human demonstrations via inverse reinforcement learning. We then "translate" this information into a natural language instruction using a neural sequence-to-sequence model that learns to generate free-form instructions from natural language corpora. We evaluate our method on a benchmark route instruction dataset and achieve a BLEU score of 72.18% when compared to human-generated reference instructions. We additionally conduct navigation experiments with human participants that demonstrate that our method generates instructions that people follow as accurately and easily as those produced by humans.

Connected through hyperedge HE-1 (induced by source paper "hierarchical decision making by generating and following natural language instructions"):
  with members:
    - "relational deep reinforcement learning"
    - "speaker follower models for vision and language navigation"
    - "hierarchical macro strategy model for moba game ai"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "unified pragmatic models for generating and following instructions"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "starcraft ii a new challenge for reinforcement learning"
    - "parlai a dialog research software platform"

Connected through hyperedge HE-2 (induced by source paper "designing a symbolic intermediate representation for neural surface realization"):
  with members:
    - "universal dependency parsing from scratch"
    - "strategies for structuring story generation"
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "neural text generation from rich semantic representations"
    - "generating high quality surface realizations using data augmentation and factored sequence models"
    - "human vs automatic metrics on the importance of correlation design"
    - "decoupling strategy and generation in negotiation dialogues"
    - "data to text generation with content selection and planning"

Connected through hyperedge HE-3 (induced by source paper "recognizing and tracking high level human meaningful navigation features of occupancy grid maps"):
  with members:
    - "translating neuralese"
    - "learning deep generative spatial models for mobile robots"
    - "yolo9000 better faster stronger"
    - "deep residual learning for image recognition"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "personalized dynamics models for adaptive assistive navigation interfaces"):
  with members:
    - "expressive robot motion timing"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "deep visual foresight for planning robot motion"
    - "deep exploration via bootstrapped dqn"
    - "learning without forgetting"
    - "a critical review of recurrent neural networks for sequence learning"
    - "recurrent network models for human dynamics"

Connected through hyperedge HE-5 (induced by source paper "extracting action sequences from texts based on deep reinforcement learning"):
  with members:
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "language understanding for text based games using deep reinforcement learning"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "codraw collaborative drawing as a testbed for grounded goal driven communication"):
  with members:
    - "speaker follower models for vision and language navigation"
    - "unified pragmatic models for generating and following instructions"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "naturalizing a programming language via interactive learning"
    - "natural language does not emerge naturally in multi agent dialog"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "embodied question answering"

Connected through hyperedge HE-7 (induced by source paper "unified pragmatic models for generating and following instructions"):
  with members:
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "context aware captions from context agnostic supervision"
    - "comprehension guided referring expressions"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "learning language games through interaction"
    - "reasoning about pragmatics with neural listeners and speakers"
    - "simpler context dependent logical forms via model projections"
    - "the neural noisy channel"

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.RO(Robotics)