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: "s trigger continual state representation learning via self triggered generative replay"
  Abstract: We consider the problem of building a state representation model for control, in a continual learning setting. As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge, and then use Reinforcement Learning on the resulting features for efficient policy learning. To this end, we propose S-TRIGGER, a general method for Continual State Representation Learning applicable to Variational Auto-Encoders and its many variants. The method is based on Generative Replay, i.e. the use of generated samples to maintain past knowledge. It comes along with a statistically sound method for environment change detection, which self-triggers the Generative Replay. Our experiments on VAEs show that S-TRIGGER learns state representations that allows fast and high-performing Reinforcement Learning, while avoiding catastrophic forgetting. The resulting system is capable of autonomously learning new information without using past data and with a bounded system size. Code for our experiments is attached in Appendix.

Connected through hyperedge HE-1 (induced by source paper "continual reinforcement learning deployed in real life using policy distillation and sim2real transfer"):
  with members:
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "continuous learning in single incremental task scenarios"
    - "state representation learning for control an overview"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "curious intrinsically motivated modular multi goal reinforcement learning"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "marginal replay vs conditional replay for continual learning"

Connected through hyperedge HE-2 (induced by source paper "discorl continual reinforcement learning via policy distillation"):
  with members:
    - "continual learning for robotics"
    - "continual reinforcement learning in 3d non stationary environments"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "continuous learning in single incremental task scenarios"
    - "state representation learning for control an overview"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "curious intrinsically motivated modular multi goal reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "symmetry based disentangled representation learning requires interaction with environments"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "on the fairness of disentangled representations"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "decoupling feature extraction from policy learning assessing benefits of state representation learning in goal based robotics"
    - "flatland a lightweight first person 2 d environment for reinforcement learning"
    - "towards a definition of disentangled representations"
    - "recurrent world models facilitate policy evolution"
    - "independently controllable features"

Connected through hyperedge HE-4 (induced by source paper "continual learning for robotics"):
  with members:
    - "training discriminative models to evaluate generative ones"
    - "towards non i i d image classification a dataset and baselines"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "roboturk a crowdsourcing platform for robotic skill learning through imitation"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "generative models from the perspective of continual learning"
    - "domain adaptation for visual applications a comprehensive survey"

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.LG(Machine Learning)