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: "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
  Abstract: Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability to learn from continuous streams of information is referred to as lifelong learning and represents a long-standing challenge for neural network models due to catastrophic forgetting in which novel sensory experience interferes with existing representations and leads to abrupt decreases in the performance on previously acquired knowledge. Computational models of lifelong learning typically alleviate catastrophic forgetting in experimental scenarios with given datasets of static images and limited complexity, thereby differing significantly from the conditions artificial agents are exposed to. In more natural settings, sequential information may become progressively available over time and access to previous experience may be restricted. Therefore, specialized neural network mechanisms are required that adapt to novel sequential experience while preventing disruptive interference with existing representations. In this paper, we propose a dual-memory self-organizing architecture for lifelong learning scenarios. The architecture comprises two growing recurrent networks with the complementary tasks of learning object instances (episodic memory) and categories (semantic memory). Both growing networks can expand in response to novel sensory experience: the episodic memory learns fine-grained spatiotemporal representations of object instances in an unsupervised fashion while the semantic memory uses task-relevant signals to regulate structural plasticity levels and develop more compact representations from episodic experience. For the consolidation of knowledge in the absence of external sensory input, the episodic memory periodically replays trajectories of neural reactivations. We evaluate the proposed model on the CORe50 benchmark dataset for continuous object recognition, showing that we significantly outperform current methods of lifelong learning in three different incremental learning scenarios.

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"
    - "on the role of neurogenesis in overcoming catastrophic forgetting"
    - "an incremental self organizing architecture for sensorimotor learning and prediction"
    - "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 "remind your neural network to prevent catastrophic forgetting"):
  with members:
    - "lifelong machine learning with deep streaming linear discriminant analysis"
    - "continual lifelong learning with neural networks a review"
    - "large scale incremental learning"
    - "overcoming catastrophic forgetting with unlabeled data in the wild"
    - "answer them all toward universal visual question answering models"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "bilinear attention networks"

Connected through hyperedge HE-3 (induced by source paper "continual lifelong learning with neural networks a review"):
  with members:
    - "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"
    - "automated curriculum learning for neural networks"

Connected through hyperedge HE-4 (induced by source paper "lifelong machine learning with deep streaming linear discriminant analysis"):
  with members:
    - "overcoming catastrophic forgetting with unlabeled data in the wild"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "revisiting distillation and incremental classifier learning"
    - "efficient lifelong learning with a gem"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "memory efficient experience replay for streaming learning"

Connected through hyperedge HE-5 (induced by source paper "fine grained continual learning"):
  with members:
    - "are we done with object recognition the icub robot s perspective"
    - "lifelong machine learning with deep streaming linear discriminant analysis"
    - "continual learning for robotics"
    - "continual lifelong learning with neural networks a review"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the limits and potentials of deep learning for robotics"
    - "continuous learning in single incremental task scenarios"

Connected through hyperedge HE-6 (induced by source paper "samples are not all useful denoising policy gradient updates using variance"):
  with members:
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "quantifying generalization in reinforcement learning"
    - "assessing generalization in deep reinforcement learning"
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "recurrent world models facilitate policy evolution"
    - "rainbow combining improvements in deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-7 (induced by source paper "a personalized affective memory neural model for improving emotion recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "the omg emotion behavior dataset"
    - "unsupervised deep representations for learning audience facial behaviors"
    - "a deep network for arousal valence emotion prediction with acoustic visual cues"
    - "multimodal emotion recognition for one minute gradual emotion challenge"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "geometry guided adversarial facial expression synthesis"

Connected through hyperedge HE-8 (induced by source paper "l3doc lifelong 3d object classification"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "deep virtual networks for memory efficient inference of multiple tasks"
    - "rotational subgroup voting and pose clustering for robust 3d object recognition"
    - "lifelong learning with dynamically expandable networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "continual learning through synaptic intelligence"
    - "icarl incremental classifier and representation learning"
    - "overcoming 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.AI(Artificial Intelligence)