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: "learning deep neural network policies with continuous memory states"
  Abstract: Policy learning for partially observed control tasks requires policies that can remember salient information from past observations. In this paper, we present a method for learning policies with internal memory for high-dimensional, continuous systems, such as robotic manipulators. Our approach consists of augmenting the state and action space of the system with continuous-valued memory states that the policy can read from and write to. Learning general-purpose policies with this type of memory representation directly is difficult, because the policy must automatically figure out the most salient information to memorize at each time step. We show that, by decomposing this policy search problem into a trajectory optimization phase and a supervised learning phase through a method called guided policy search, we can acquire policies with effective memorization and recall strategies. Intuitively, the trajectory optimization phase chooses the values of the memory states that will make it easier for the policy to produce the right action in future states, while the supervised learning phase encourages the policy to use memorization actions to produce those memory states. We evaluate our method on tasks involving continuous control in manipulation and navigation settings, and show that our method can learn complex policies that successfully complete a range of tasks that require memory.

Connected through hyperedge HE-1 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explainable reinforcement learning through a causal lens"
    - "atari head atari human eye tracking and demonstration dataset"
    - "conservative q improvement reinforcement learning for an interpretable decision tree policy"
    - "free lunch saliency via attention in atari agents"
    - "reinforcement learning with attention that works a self supervised approach"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "agil learning attention from human for visuomotor tasks"

Connected through hyperedge HE-2 (induced by source paper "self organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks"):
  with members:
    - "model based reinforcement learning for atari"
    - "learning abstract options"
    - "a novel predictive coding inspired variational rnn model for online prediction and recognition"
    - "bayesian model agnostic meta learning"
    - "one big net for everything"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "probabilistic model agnostic meta learning"
    - "recurrent world models facilitate policy evolution"

Connected through hyperedge HE-3 (induced by source paper "scene memory transformer for embodied agents in long horizon tasks"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "active neural localization"
    - "learning longer term dependencies in rnns with auxiliary losses"

Connected through hyperedge HE-4 (induced by source paper "learning belief representations for imitation learning in pomdps"):
  with members:
    - "neural predictive belief representations"
    - "learning and querying fast generative models for reinforcement learning"
    - "deep variational reinforcement learning for pomdps"
    - "recurrent world models facilitate policy evolution"
    - "representation learning with contrastive predictive coding"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-5 (induced by source paper "exploration via flow based intrinsic rewards"):
  with members:
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "count based exploration with neural density models"
    - "visualizing and understanding atari agents"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "curiosity driven exploration by self supervised prediction"
    - "proximal policy optimization algorithms"
    - "deep network flow for multi object tracking"

Connected through hyperedge HE-6 (induced by source paper "never forget balancing exploration and exploitation via learning optical flow"):
  with members:
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "measuring catastrophic forgetting in neural networks"
    - "noisy networks for exploration"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "curiosity driven exploration by self supervised prediction"
    - "deep network flow for multi object tracking"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-7 (induced by source paper "regularized anderson acceleration for off policy deep reinforcement learning"):
  with members:
    - "soft policy gradient method for maximum entropy deep reinforcement learning"
    - "multi pseudo q learning based deterministic policy gradient for tracking control of autonomous underwater vehicles"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "addressing function approximation error in actor critic methods"
    - "anderson acceleration for reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "averaged dqn variance reduction and stabilization for deep reinforcement learning"
    - "sample efficient actor critic with experience replay"

Connected through hyperedge HE-8 (induced by source paper "clustered reinforcement learning"):
  with members:
    - "exploration by random network distillation"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "count based exploration with neural density models"
    - "curiosity driven exploration by self supervised prediction"
    - "exploratory gradient boosting for reinforcement learning in complex domains"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "vime variational information maximizing exploration"
    - "unifying count based exploration and intrinsic motivation"

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)