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: "learning interpretable spatial operations in a rich 3d blocks world"
  Abstract: In this paper, we study the problem of mapping natural language instructions to complex spatial actions in a 3D blocks world. We first introduce a new dataset that pairs complex 3D spatial operations to rich natural language descriptions that require complex spatial and pragmatic interpretations such as "mirroring", "twisting", and "balancing". This dataset, built on the simulation environment of Bisk, Yuret, and Marcu (2016), attains language that is significantly richer and more complex, while also doubling the size of the original dataset in the 2D environment with 100 new world configurations and 250,000 tokens. In addition, we propose a new neural architecture that achieves competitive results while automatically discovering an inventory of interpretable spatial operations (Figure 5)

Connected through hyperedge HE-1 (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-2 (induced by source paper "raven a dataset for relational and analogical visual reasoning"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "compositional attention networks for machine reasoning"
    - "visual question reasoning on general dependency tree"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "stay on the path instruction fidelity in vision and language navigation"):
  with members:
    - "multi modal discriminative model for vision and language navigation"
    - "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"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "chalet cornell house agent learning environment"
    - "speaker follower models for vision and language navigation"
    - "on evaluation of embodied navigation agents"

Connected through hyperedge HE-4 (induced by source paper "improving robot success detection using static object data"):
  with members:
    - "robocse robot common sense embedding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "a simple neural network module for relational reasoning"
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"

Connected through hyperedge HE-5 (induced by source paper "prospection interpretable plans from language by predicting the future"):
  with members:
    - "universal planning networks"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "evaluating methods for end user creation of robot task plans"
    - "visual robot task planning"
    - "learning model based planning from scratch"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "simulating action dynamics with neural process networks"
    - "imagination augmented agents for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "improving grounded natural language understanding through human robot dialog"):
  with members:
    - "teaching robots to imitate a human with no on teacher sensors what are the key challenges"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "neural amr sequence to sequence models for parsing and generation"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "language to logical form with neural attention"
    - "data recombination for neural semantic parsing"

Connected through hyperedge HE-7 (induced by source paper "a spoken dialogue system for spatial question answering in a physical blocks world"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "a type coherent expressive representation as an initial step to language understanding"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "acquiring common sense spatial knowledge through implicit spatial templates"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-8 (induced by source paper "vision based navigation with language based assistance via imitation learning with indirect intervention"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "virtualhome simulating household activities via programs"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "textworld a learning environment for text based games"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "gibson env real world perception for embodied agents"

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)