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: "a neurorobotic experiment for crossmodal conflict resolution in complex environments"
  Abstract: Crossmodal conflict resolution is a crucial component of robot sensorimotor coupling through interaction with the environment for swift and robust behaviour also in noisy conditions. In this paper, we propose a neurorobotic experiment in which an iCub robot exhibits human-like responses in a complex crossmodal environment. To better understand how humans deal with multisensory conflicts, we conducted a behavioural study exposing 33 subjects to congruent and incongruent dynamic audio-visual cues. In contrast to previous studies using simplified stimuli, we designed a scenario with four animated avatars and observed that the magnitude and extension of the visual bias are related to the semantics embedded in the scene, i.e., visual cues that are congruent with environmental statistics (moving lips and vocalization) induce a stronger bias. We propose a deep learning model that processes stereophonic sound, facial features, and body motion to trigger a discrete response resembling the collected behavioural data. After training, we exposed the iCub to the same experimental conditions as the human subjects, showing that the robot can replicate similar responses in real time. Our interdisciplinary work provides important insights into how crossmodal conflict resolution can be modelled in robots and introduces future research directions for the efficient combination of sensory drive with internally generated knowledge and expectations.

Connected through hyperedge HE-1 (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"
    - "deep generative dual memory network for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "expectation learning for adaptive crossmodal stimuli association"
    - "automated curriculum learning for neural networks"

Connected through hyperedge HE-2 (induced by source paper "what can computational models learn from human selective attention a review from an audiovisual crossmodal perspective"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "a self attention based deep learning method for lesion attribute detection from ct reports"
    - "enhanced robot speech recognition using biomimetic binaural sound source localization"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "cross modal attentional context learning for rgb d object detection"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "end to end saliency mapping via probability distribution prediction"
    - "assessing the contribution of semantic congruency to multisensory integration and conflict resolution"

Connected through hyperedge HE-3 (induced by source paper "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "continuous learning in single incremental task scenarios"
    - "deep generative dual memory network for continual learning"
    - "progressive neural networks"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "continual learning through synaptic intelligence"
    - "neurogenesis deep learning extending deep networks to accommodate new classes"
    - "measuring catastrophic forgetting in neural networks"

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)