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: "listen attend and walk neural mapping of navigational instructions to action sequences"
  Abstract: We propose a neural sequence-to-sequence model for direction following, a task that is essential to realizing effective autonomous agents. Our alignment-based encoder-decoder model with long short-term memory recurrent neural networks (LSTM-RNN) translates natural language instructions to action sequences based upon a representation of the observable world state. We introduce a multi-level aligner that empowers our model to focus on sentence "regions" salient to the current world state by using multiple abstractions of the input sentence. In contrast to existing methods, our model uses no specialized linguistic resources (e.g., parsers) or task-specific annotations (e.g., seed lexicons). It is therefore generalizable, yet still achieves the best results reported to-date on a benchmark single-sentence dataset and competitive results for the limited-training multi-sentence setting. We analyze our model through a series of ablations that elucidate the contributions of the primary components of our model.

Connected through hyperedge HE-1 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "measuring compositionality in representation learning"
    - "the emergence of number and syntax units in lstm language models"
    - "what do rnn language models learn about filler gap dependencies"
    - "on adversarial examples for character level neural machine translation"
    - "distinct patterns of syntactic agreement errors in recurrent networks and humans"
    - "jump to better conclusions scan both left and right"

Connected through hyperedge HE-2 (induced by source paper "a benchmark for systematic generalization in grounded language understanding"):
  with members:
    - "learning compositional rules via neural program synthesis"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "emergent systematic generalization in a situated agent"
    - "compositional generalization through meta sequence to sequence learning"
    - "good enough compositional data augmentation"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "modeling question asking using neural program generation"
    - "babyai a platform to study the sample efficiency of grounded language learning"

Connected through hyperedge HE-3 (induced by source paper "combating false negatives in adversarial imitation learning"):
  with members:
    - "task relevant adversarial imitation learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "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"

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 "revisiting embodiedqa a simple baseline and beyond"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "multi target embodied question answering"
    - "active transfer learning network a unified deep joint spectral spatial feature learning model for hyperspectral image classification"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "blindfold baselines for embodied qa"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "neural modular control for embodied question answering"
    - "a better way to attend attention with trees for video question answering"

Connected through hyperedge HE-6 (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"
    - "guided feature transformation gft a neural language grounding module for embodied agents"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "learning to navigate unseen environments back translation with environmental dropout"):
  with members:
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "speaker follower models for vision and language navigation"
    - "on evaluation of embodied navigation agents"
    - "style transfer through back translation"
    - "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"
    - "understanding back translation at scale"

Connected through hyperedge HE-8 (induced by source paper "executing instructions in situated collaborative interactions"):
  with members:
    - "meetup a corpus of joint activity dialogues in a visual environment"
    - "hierarchical decision making by generating and following natural language instructions"
    - "a natural language corpus of common grounding under continuous and partially observable context"
    - "learning to map context dependent sentences to executable formal queries"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "decoupling strategy and generation in negotiation dialogues"
    - "situated mapping of sequential instructions to actions with single step reward observation"

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)