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: "decoupling feature extraction from policy learning assessing benefits of state representation learning in goal based robotics"
  Abstract: Scaling end-to-end reinforcement learning to control real robots from vision presents a series of challenges, in particular in terms of sample efficiency. Against end-to-end learning, state representation learning can help learn a compact, efficient and relevant representation of states that speeds up policy learning, reducing the number of samples needed, and that is easier to interpret. We evaluate several state representation learning methods on goal based robotics tasks and propose a new unsupervised model that stacks representations and combines strengths of several of these approaches. This method encodes all the relevant features, performs on par or better than end-to-end learning with better sample efficiency, and is robust to hyper-parameters change.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "learning group structure and disentangled representations of dynamical environments"):
  with members:
    - "hamiltonian generative networks"
    - "symmetry based disentangled representation learning requires interaction with environments"
    - "hamiltonian neural networks"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "a style based generator architecture for generative adversarial networks"
    - "neural ordinary differential equations"
    - "towards a definition of disentangled representations"
    - "a survey of inductive biases for factorial representation learning"

Connected through hyperedge HE-3 (induced by source paper "s trigger continual state representation learning via self triggered generative replay"):
  with members:
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "state representation learning for control an overview"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "unsupervised predictive memory in a goal directed agent"
    - "flatland a lightweight first person 2 d environment for reinforcement learning"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "associative compression networks for representation learning"
    - "generative models from the perspective of continual learning"

Connected through hyperedge HE-4 (induced by source paper "symmetry based disentangled representation learning requires interaction with environments"):
  with members:
    - "s trigger continual state representation learning via self triggered generative replay"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "on the fairness of disentangled representations"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "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"

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)