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: "deep reinforcement learning in large discrete action spaces"
  Abstract: Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems. Recommender systems, industrial plants and language models are only some of the many real-world tasks involving large numbers of discrete actions for which current methods are difficult or even often impossible to apply. An ability to generalize over the set of actions as well as sub-linear complexity relative to the size of the set are both necessary to handle such tasks. Current approaches are not able to provide both of these, which motivates the work in this paper. Our proposed approach leverages prior information about the actions to embed them in a continuous space upon which it can generalize. Additionally, approximate nearest-neighbor methods allow for logarithmic-time lookup complexity relative to the number of actions, which is necessary for time-wise tractable training. This combined approach allows reinforcement learning methods to be applied to large-scale learning problems previously intractable with current methods. We demonstrate our algorithm’s abilities on a series of tasks having up to one million actions.

Connected through hyperedge HE-1 (induced by source paper "i love your chain mail making knights smile in a fantasy game world open domain goal orientated dialogue agents"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "hierarchical decision making by generating and following natural language instructions"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "learning to speak and act in a fantasy text adventure game"
    - "countering language drift via visual grounding"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "target guided open domain conversation"
    - "learning semantic textual similarity from conversations"

Connected through hyperedge HE-2 (induced by source paper "jointly learning to recommend and advertise"):
  with members:
    - "solving continual combinatorial selection via deep reinforcement learning"
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "a multi agent reinforcement learning method for impression allocation in online display advertising"
    - "learning to advertise with adaptive exposure via constrained two level reinforcement learning"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "reinforcement learning based recommender system using biclustering technique"

Connected through hyperedge HE-3 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning"):
  with members:
    - "ieee 802 11be extremely high throughput the next generation of wi fi technology beyond 802 11ax"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "deep reinforcement learning for resource management in network slicing"
    - "experience driven networking a deep reinforcement learning based approach"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "progressive neural architecture search"
    - "hindsight experience replay"
    - "a deep reinforcement learning based framework for content caching"

Connected through hyperedge HE-4 (induced by source paper "parameterizing branch and bound search trees to learn branching policies"):
  with members:
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "learning action representations for reinforcement learning"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "exgate externally controlled gating for feature based attention in artificial neural networks"
    - "beyond skip connections top down modulation for object detection"
    - "feature pyramid networks for object detection"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "viewport aware deep reinforcement learning approach for 360 o video caching"):
  with members:
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "cost effective cache deployment in mobile heterogeneous networks"
    - "saltinet scan path prediction on 360 degree images using saliency volumes"
    - "edge caching wireless networks performance analysis and optimization"
    - "deep reinforcement learning framework applications and embedded implementations"
    - "a deep reinforcement learning based framework for content caching"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "caching at the wireless edge design aspects challenges and future directions"

Connected through hyperedge HE-6 (induced by source paper "model free vision based shaping of deformable plastic materials"):
  with members:
    - "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "learning robust bed making using deep imitation learning with dart"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "active perception and modeling of deformable surfaces using gaussian processes and position based dynamics"

Connected through hyperedge HE-7 (induced by source paper "a theoretical analysis of deep q learning"):
  with members:
    - "a theory of regularized markov decision processes"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "dynamics aware embeddings"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "model based reinforcement learning for atari"
    - "learning action representations for reinforcement learning"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "the natural language of actions"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"

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)