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: "on improving deep reinforcement learning for pomdps"
  Abstract: Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e.g., computer Go. However, very little work has been done in deep RL to handle partially observable environments. We propose a new architecture called Action-specific Deep Recurrent Q-Network (ADRQN) to enhance learning performance in partially observable domains. Actions are encoded by a fully connected layer and coupled with a convolutional observation to form an action-observation pair. The time series of action-observation pairs are then integrated by an LSTM layer that learns latent states based on which a fully connected layer computes Q-values as in conventional Deep Q-Networks (DQNs). We demonstrate the effectiveness of our new architecture in several partially observable domains, including flickering Atari games.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"

Connected through hyperedge HE-2 (induced by source paper "stochastic latent actor critic deep reinforcement learning with a latent variable model"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "deepmdp learning continuous latent space models for representation learning"
    - "shaping belief states with generative environment models for rl"
    - "deepmind control suite"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning latent dynamics for planning from pixels"
    - "visual reinforcement learning with imagined goals"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"

Connected through hyperedge HE-3 (induced by source paper "optimizing sequential medical treatments with auto encoding heuristic search in pomdps"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "representation balancing mdps for off policy policy evaluation"
    - "distributed prioritized experience replay"
    - "deep variational reinforcement learning for pomdps"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "distributed distributional deterministic policy gradients"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "filtering variational objectives"

Connected through hyperedge HE-4 (induced by source paper "inductive bias driven reinforcement learning for efficient schedules in heterogeneous clusters"):
  with members:
    - "learning scheduling algorithms for data processing clusters"
    - "asap accelerated short read alignment on programmable hardware"
    - "relational inductive biases deep learning and graph networks"
    - "deep variational reinforcement learning for pomdps"
    - "qmdp net deep learning for planning under partial observability"
    - "the predictron end to end learning and planning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "dual sequential monte carlo tunneling filtering and planning in continuous pomdps"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "integrating algorithmic planning and deep learning for partially observable navigation"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "spectral normalization for generative adversarial networks"
    - "particle filter networks with application to visual localization"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep variational reinforcement learning for pomdps"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-6 (induced by source paper "robust reinforcement learning in pomdps with incomplete and noisy observations"):
  with members:
    - "deep variational reinforcement learning for pomdps"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "proximal policy optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"
    - "deep recurrent q learning for partially observable mdps"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "multi agent deep reinforcement learning with human strategies"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "multi agent deep reinforcement learning with extremely noisy observations"
    - "a human mixed strategy approach to deep reinforcement learning"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "bayesian reinforcement learning in factored pomdps"):
  with members:
    - "deep variational reinforcement learning for pomdps"
    - "learning in pomdps with monte carlo tree search"
    - "efficient exploration through bayesian deep q networks"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "approximate planning for factored pomdps using belief state simplification"
    - "playing atari with deep reinforcement learning"

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)