human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "investigation of multimodal and agential interactions in human robot imitation based on frameworks of predictive coding and active inference"):
  Abstract: This study proposes a model for multimodal, imitative interaction of agents, based on frameworks of predictive coding and active inference, using a variational Bayes recurrent neural network. The model dynamically predicts visual sensation and proprioception simultaneously through generative processes by associating both modalities. It also updates the internal state and generates actions by maximizing the lower bound. A key feature of the model is that the complexity of each modality, as well as of the entire network can be regulated independently. We hypothesize that regulation of complexity offers a common perspective over two distinct properties of embodied agents: coordination of multimodalities and strength of agent intention or belief in social interactions. We evaluate the hypotheses by conducting experiments on imitative human-robot interactions in two different scenarios using the model. First, regulation of complexity was changed between the vision module and the proprioception module during learning. The results showed that complexity of the vision module should be more strongly regulated than that of proprioception because of its greater randomness. Second, the strength of complexity regulation of the whole network in the robot was varied during test imitation after learning. We found that this affects human-robot interactions significantly. With weaker regulation of complexity, the robot tends to move more egocentrically, without adapting to the human counterpart. On the other hand, with stronger regulation, the robot tends to follow its human counterpart by adapting its internal state. Our study concludes that the strength with which complexity is regulated significantly affects the nature of dynamic interactions between different modalities and between individual agents in a social setting.

Includes members:
  Member "active inference body perception and action for humanoid robots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "end to end pixel based deep active inference for body perception and action")
  Member "cognitive and motor compliance in intentional human robot interaction":
    also connected through other hyperedges:
      (none)
  Member "a novel predictive coding inspired variational rnn model for online prediction and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "minor constraint disturbances for deep semi supervised learning")
  Member "an intriguing failing of convolutional neural networks and the coordconv solution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "a recurrent latent variable model for sequential data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic latent residual video prediction")
  Member "convolutional lstm network a machine learning approach for precipitation nowcasting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)