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: "an incremental self organizing architecture for sensorimotor learning and prediction"
  Abstract: During visuomotor tasks, robots have to compensate for the temporal delays inherent in their sensorimotor processing systems. This capability becomes crucial in a dynamic environment where the visual input is constantly changing, e.g. when interacting with humans. For this purpose, the robot should be equipped with a prediction mechanism able to use the acquired perceptual experience in order to estimate possible future motor commands. In this paper, we present a novel neural network architecture that learns prototypical visuomotor representations and provides reliable predictions to compensate for the delayed robot behavior in an online manner. We investigate the performance of our method in the context of a synchronization task, where a humanoid robot has to generate visually perceived arm motion trajectories in synchrony with a human demonstrator. We evaluate the prediction accuracy in terms of mean prediction error and analyze the response of the network to novel movement demonstrations. Additionally, we provide experiments with the system receiving incomplete data sequences, showing the robustness of the proposed architecture in the case of a noisy and faulty visual sensor.

Connected through hyperedge HE-1 (induced by source paper "human action recognition and assessment via deep neural network self organization"):
  with members:
    - "rethinking continual learning for autonomous agents and robots"
    - "continual lifelong learning with neural networks a review"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "on the role of neurogenesis in overcoming catastrophic forgetting"
    - "a self organizing neural network architecture for learning human object interactions"
    - "continual learning through synaptic intelligence"
    - "icarl incremental classifier and representation learning"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-2 (induced by source paper "continual lifelong learning with neural networks a review"):
  with members:
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "continuous learning in single incremental task scenarios"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "on the role of neurogenesis in overcoming catastrophic forgetting"
    - "a neurorobotic experiment for crossmodal conflict resolution in complex environments"
    - "deep generative dual memory network for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "expectation learning for adaptive crossmodal stimuli association"

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.CV(Computer Vision and Pattern Recognition)