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: "distributional reinforcement learning with quantile regression"
  Abstract: In reinforcement learning an agent interacts with the environment by taking actions and observing the next state and reward. When sampled probabilistically, these state transitions, rewards, and actions can all induce randomness in the observed long-term return. Traditionally, reinforcement learning algorithms average over this randomness to estimate the value function. In this paper, we build on recent work advocating a distributional approach to reinforcement learning in which the distribution over returns is modeled explicitly instead of only estimating the mean. That is, we examine methods of learning the value distribution instead of the value function. We give results that close a number of gaps between the theoretical and algorithmic results given by Bellemare, Dabney, and Munos (2017). First, we extend existing results to the approximate distribution setting. Second, we present a novel distributional reinforcement learning algorithm consistent with our theoretical formulation. Finally, we evaluate this new algorithm on the Atari 2600 games, observing that it significantly outperforms many of the recent improvements on DQN, including the related distributional algorithm C51.

Connected through hyperedge HE-1 (induced by source paper "artificial intelligence for prosthetics challenge solutions"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning to run challenge synthesizing physiologically accurate motion using deep reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "distributed prioritized experience replay"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning to run challenge solutions adapting reinforcement learning methods for neuromusculoskeletal environments"
    - "distributed distributional deterministic policy gradients"

Connected through hyperedge HE-2 (induced by source paper "an optimistic perspective on offline reinforcement learning"):
  with members:
    - "keep doing what worked behavioral modelling priors for offline reinforcement learning"
    - "benchmarking batch deep reinforcement learning algorithms"
    - "off policy policy gradient with state distribution correction"
    - "behavior regularized offline reinforcement learning"
    - "when to use parametric models in reinforcement learning"
    - "challenges of real world reinforcement learning"
    - "model based reinforcement learning for atari"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"

Connected through hyperedge HE-3 (induced by source paper "millimeter wave communications with an intelligent reflector performance optimization and distributional reinforcement learning"):
  with members:
    - "intelligent reflecting surface meets ofdm protocol design and rate maximization"
    - "reflections in the sky millimeter wave communication with uav carried intelligent reflectors"
    - "reflect and transmit array antennas for scalable and energy efficient mmwave massive mimo"
    - "intelligent reflecting and transmitting surface aided millimeter wave massive mimo"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "performance analysis of large intelligent surfaces liss uplink spectral efficiency and pilot training"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"

Connected through hyperedge HE-4 (induced by source paper "sample based distributional policy gradient"):
  with members:
    - "gan based deep distributional reinforcement learning for resource management in network slicing"
    - "deepmind control suite"
    - "quota the quantile option architecture for reinforcement learning"
    - "nonlinear distributional gradient temporal difference learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "distributed distributional deterministic policy gradients"
    - "generative modeling using the sliced wasserstein distance"
    - "distributional multivariate policy evaluation and exploration with the bellman gan"

Connected through hyperedge HE-5 (induced by source paper "addressing value estimation errors in reinforcement learning with a state action return distribution function"):
  with members:
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "distributed prioritized experience replay"
    - "equivalence between policy gradients and soft q learning"
    - "a distributional perspective on reinforcement learning"
    - "emergence of locomotion behaviours in rich environments"
    - "bridging the gap between value and policy based reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "deep learning tubes for tube mpc"):
  with members:
    - "regression via arbitrary quantile modeling"
    - "robust regression for safe exploration in control"
    - "monotonic trends in deep neural networks"
    - "a general framework for uncertainty estimation in deep learning"
    - "learning control lyapunov functions from counterexamples and demonstrations"
    - "linear model predictive safety certification for learning based control"
    - "learning based model predictive control for safe exploration"
    - "cautious model predictive control using gaussian process regression"

Connected through hyperedge HE-7 (induced by source paper "deepmdp learning continuous latent space models for representation learning"):
  with members:
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "model based reinforcement learning for atari"
    - "the value function polytope in reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "combined reinforcement learning via abstract representations"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-8 (induced by source paper "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "learning powerful policies by using consistent dynamics model"
    - "mega reward achieving human level play without extrinsic rewards"
    - "model based reinforcement learning for atari"
    - "strategic object oriented reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"
    - "learning plannable representations with causal infogan"

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.AI(Artificial Intelligence)