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: "unifying task specification in reinforcement learning"
  Abstract: Reinforcement learning tasks are typically specified as Markov decision processes. This formalism has been highly successful, though specifications often couple the dynamics of the environment and the learning objective. This lack of modularity can complicate generalization of the task specification, as well as obfuscate connections between different task settings, such as episodic and continuing. In this work, we introduce the RL task formalism, that provides a unification through simple constructs including a generalization to transition-based discounting. Through a series of examples, we demonstrate the generality and utility of this formalism. Finally, we extend standard learning constructs, including Bellman operators, and extend some seminal theoretical results, including approximation errors bounds. Overall, we provide a well-understood and sound formalism on which to build theoretical results and simplify algorithm use and development.

Connected through hyperedge HE-1 (induced by source paper "options of interest temporal abstraction with interest functions"):
  with members:
    - "the termination critic"
    - "feudal networks for hierarchical reinforcement learning"
    - "learnings options end to end for continuous action tasks"
    - "when waiting is not an option learning options with a deliberation cost"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "adaptive skills adaptive partitions asap"
    - "option discovery in hierarchical reinforcement learning using spatio temporal clustering"
    - "the option critic architecture"

Connected through hyperedge HE-2 (induced by source paper "generalized off policy actor critic"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "convergent actor critic algorithms under off policy training and function approximation"
    - "online off policy prediction"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-3 (induced by source paper "fast efficient hyperparameter tuning for policy gradients"):
  with members:
    - "meta gradient reinforcement learning"
    - "bayesian optimization in alphago"
    - "deep variational reinforcement learning for pomdps"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "where did my optimum go an empirical analysis of gradient descent optimization in policy gradient methods"
    - "deep reinforcement learning that matters"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "gamma nets generalizing value estimation over timescale"):
  with members:
    - "separating value functions across time scales"
    - "dopamine a research framework for deep reinforcement learning"
    - "meta gradient reinforcement learning"
    - "unicorn continual learning with a universal off policy agent"
    - "universal successor representations for transfer reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "successor features for transfer in reinforcement learning"
    - "learning values across many orders of magnitude"

Connected through hyperedge HE-5 (induced by source paper "overcoming catastrophic interference in online reinforcement learning with dynamic self organizing maps"):
  with members:
    - "continual learning with self organizing maps"
    - "two geometric input transformation methods for fast online reinforcement learning with neural nets"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "the utility of sparse representations for control in reinforcement learning"
    - "overcoming catastrophic forgetting in neural networks"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "discounted reinforcement learning is not an optimization problem"):
  with members:
    - "issues concerning realizability of blackwell optimal policies in reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "value propagation networks"):
  with members:
    - "the bottleneck simulator a model based deep reinforcement learning approach"
    - "cooperative motion planning for non holonomic agents with value iteration networks"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "learning generalized reactive policies using deep neural networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "value prediction network"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "generalized value iteration networks life beyond lattices"

Connected through hyperedge HE-8 (induced by source paper "combined reinforcement learning via abstract representations"):
  with members:
    - "decoupling dynamics and reward for transfer learning"
    - "mine mutual information neural estimation"
    - "rainbow combining improvements in deep reinforcement learning"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "the consciousness prior"
    - "value prediction network"
    - "a distributional perspective on reinforcement learning"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"

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)