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: "language bootstrapping learning word meanings from perception action association"
  Abstract: We address the problem of bootstrapping language acquisition for an artificial system similarly to what is observed in experiments with human infants. Our method works by associating meanings to words in manipulation tasks, as a robot interacts with objects and listens to verbal descriptions of the interactions. The model is based on an affordance network, i.e., a mapping between robot actions, robot perceptions, and the perceived effects of these actions upon objects. We extend the affordance model to incorporate spoken words, which allows us to ground the verbal symbols to the execution of actions and the perception of the environment. The model takes verbal descriptions of a task as the input and uses temporal co-occurrence to create links between speech utterances and the involved objects, actions, and effects. We show that the robot is able form useful word-to-meaning associations, even without considering grammatical structure in the learning process and in the presence of recognition errors. These word-to-meaning associations are embedded in the robot's own understanding of its actions. Thus, they can be directly used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task. We believe that the encouraging results with our approach may afford robots with a capacity to acquire language descriptors in their operation's environment as well as to shed some light as to how this challenging process develops with human infants.

Connected through hyperedge HE-1 (induced by source paper "a computational model of early language acquisition from audiovisual experiences of young infants"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "computational and robotic models of early language development a review"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "the zero resource speech challenge 2017"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-2 (induced by source paper "beyond the self using grounded affordances to interpret and describe others actions"):
  with members:
    - "a simple neural network module for relational reasoning"
    - "interactive robot learning of gestures language and affordances"
    - "building machines that learn and think like people"
    - "learning human activities and object affordances from rgb d videos"
    - "the symbol grounding problem"

Connected through hyperedge HE-3 (induced by source paper "systems of natural language facilitated human robot cooperation a review"):
  with members:
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "understanding user instructions by utilizing open knowledge for service robots"
    - "generating machine executable plans from end user s natural language instructions"
    - "learning models for following natural language directions in unknown environments"
    - "neural machine translation by jointly learning to align and translate"
    - "deep visual semantic alignments for generating image descriptions"
    - "generalized grounding graphs a probabilistic framework for understanding grounded commands"

Connected through hyperedge HE-4 (induced by source paper "methodologies for realizing natural language facilitated human robot cooperation a review"):
  with members:
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "generating machine executable plans from end user s natural language instructions"
    - "learning models for following natural language directions in unknown environments"
    - "latent hierarchical model for activity recognition"
    - "a joint model of language and perception for grounded attribute learning"
    - "multi modal human machine communication for instructing robot grasping tasks"

Connected through hyperedge HE-5 (induced by source paper "self supervised vision based detection of the active speaker as a prerequisite for socially aware language acquisition"):
  with members:
    - "look listen and learn a multimodal lstm for speaker identification"
    - "deep multimodal speaker naming"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)