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: "robot language learning generation and comprehension"
  Abstract: We present a unified framework which supports grounding natural-language semantics in robotic driving. This framework supports acquisition (learning grounded meanings of nouns and prepositions from human annotation of robotic driving paths), generation (using such acquired meanings to generate sentential description of new robotic driving paths), and comprehension (using such acquired meanings to support automated driving to accomplish navigational goals specified in natural language). We evaluate the performance of these three tasks by having independent human judges rate the semantic fidelity of the sentences associated with paths, achieving overall average correctness of 94.6% and overall average completeness of 85.6%.

Connected through hyperedge HE-1 (induced by source paper "interactive grounded language acquisition and generalization in a 2d world"):
  with members:
    - "modulating early visual processing by language"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning to reason end to end module networks for visual question answering"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "grounding of textual phrases in images by reconstruction"
    - "learning to compose neural networks for question answering"

Connected through hyperedge HE-2 (induced by source paper "a deep compositional framework for human like language acquisition in virtual environment"):
  with members:
    - "learning to reason end to end module networks for visual question answering"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "grounding of textual phrases in images by reconstruction"
    - "learning to compose neural networks for question answering"
    - "hierarchical question image co attention for visual question answering"
    - "building machines that learn and think like people"
    - "zero shot visual question answering"

Connected through hyperedge HE-3 (induced by source paper "exploiting deep semantics and compositionality of natural language for human robot interaction"):
  with members:
    - "robust natural language processing combining reasoning cognitive semantics and construction grammar for spatial language"

Connected through hyperedge HE-4 (induced by source paper "generation and comprehension of unambiguous object descriptions"):
  with members:
    - "lstm a search space odyssey"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "vqa visual question answering"
    - "densecap fully convolutional localization networks for dense captioning"
    - "exploring nearest neighbor approaches for image captioning"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "ask your neurons a neural based approach to answering questions about images"
    - "natural language object retrieval"

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)