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: "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
  Abstract: Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These exploration algorithms have been shown to allow real world robots to acquire skills such as tool use in high-dimensional continuous state and action spaces. However, they have so far assumed that self-generated goals are sampled in a specifically engineered feature space, limiting their autonomy. In this work, we propose to use deep representation learning algorithms to learn an adequate goal space. This is a developmental 2-stage approach: first, in a perceptual learning stage, deep learning algorithms use passive raw sensor observations of world changes to learn a corresponding latent space; then goal exploration happens in a second stage by sampling goals in this latent space. We present experiments where a simulated robot arm interacts with an object, and we show that exploration algorithms using such learned representations can match the performance obtained using engineered representations.

Connected through hyperedge HE-1 (induced by source paper "off policy deep reinforcement learning with analogous disentangled exploration"):
  with members:
    - "mulex disentangling exploitation from exploration in deep rl"
    - "importance resampling for off policy prediction"
    - "off policy deep reinforcement learning without exploration"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "parameter space noise for exploration"

Connected through hyperedge HE-2 (induced by source paper "scaling all goals updates in reinforcement learning using convolutional neural networks"):
  with members:
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "many goals reinforcement learning"
    - "time limits in reinforcement learning"
    - "action branching architectures for deep reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "value iteration networks"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "exploration via hindsight goal generation"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "skew fit state covering self supervised reinforcement learning"
    - "maximum entropy regularized multi goal reinforcement learning"
    - "addressing sample complexity in visual tasks using her and hallucinatory gans"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "curiosity driven multi criteria hindsight experience replay"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "accuracy based curriculum learning in deep reinforcement learning"
    - "exploration by random network distillation"
    - "improvements on hindsight learning"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "curiosity driven experience prioritization via density estimation"

Connected through hyperedge HE-5 (induced by source paper "skew fit state covering self supervised reinforcement learning"):
  with members:
    - "self supervised learning of image embedding for continuous control"
    - "provably efficient maximum entropy exploration"
    - "exploration by random network distillation"
    - "temporal difference models model free deep rl for model based control"
    - "unsupervised meta learning for reinforcement learning"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "large scale study of curiosity driven learning"

Connected through hyperedge HE-6 (induced by source paper "maximum entropy regularized multi goal reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "visual reinforcement learning with imagined goals"
    - "curiosity driven experience prioritization via density estimation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "latent space policies for hierarchical reinforcement learning"
    - "energy based hindsight experience prioritization"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-7 (induced by source paper "adapting behaviour via intrinsic reward a survey and empirical study"):
  with members:
    - "learning feature relevance through step size adaptation in temporal difference learning"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "curiosity driven reinforcement learning with homeostatic regulation"
    - "automated curriculum learning for neural networks"
    - "hindsight experience replay"
    - "teacher student curriculum learning"

Connected through hyperedge HE-8 (induced by source paper "intrinsically motivated discovery of diverse patterns in self organizing systems"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "skew fit state covering self supervised reinforcement learning"
    - "exploration by random network distillation"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "large scale study of curiosity driven learning"
    - "isolating sources of disentanglement in variational autoencoders"
    - "curiosity driven exploration of learned disentangled goal spaces"

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)