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: "a geometric perspective on optimal representations for reinforcement learning"
  Abstract: We propose a new perspective on representation learning in reinforcement learning based on geometric properties of the space of value functions. We leverage this perspective to provide formal evidence regarding the usefulness of value functions as auxiliary tasks. Our formulation considers adapting the representation to minimize the (linear) approximation of the value function of all stationary policies for a given environment. We show that this optimization reduces to making accurate predictions regarding a special class of value functions which we call adversarial value functions (AVFs). We demonstrate that using value functions as auxiliary tasks corresponds to an expected-error relaxation of our formulation, with AVFs a natural candidate, and identify a close relationship with proto-value functions (Mahadevan, 2005). We highlight characteristics of AVFs and their usefulness as auxiliary tasks in a series of experiments on the four-room domain.

Connected through hyperedge HE-1 (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"
    - "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"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"

Connected through hyperedge HE-2 (induced by source paper "discovery of useful questions as auxiliary tasks"):
  with members:
    - "general non linear bellman equations"
    - "on inductive biases in deep reinforcement learning"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "unsupervised meta learning for reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "meta gradient reinforcement learning"
    - "unicorn continual learning with a universal off policy agent"
    - "unsupervised learning via meta learning"

Connected through hyperedge HE-3 (induced by source paper "meta answering for machine reading"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "answering while summarizing multi task learning for multi hop qa with evidence extraction"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "a bert baseline for the natural questions"
    - "probing neural network comprehension of natural language arguments"
    - "episodic memory reader learning what to remember for question answering from streaming data"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-4 (induced by source paper "striving for simplicity in off policy deep reinforcement learning"):
  with members:
    - "distributional reinforcement learning with linear function approximation"
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "off policy evaluation via off policy classification"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "dopamine a research framework for deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "implicit quantile networks for distributional reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "agent modeling as auxiliary task for deep reinforcement learning"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "backplay man muss immer umkehren"
    - "deep reinforcement learning for general video game ai"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "machine theory of mind"
    - "a deep policy inference q network for multi agent systems"
    - "lenient multi agent deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "learning representations in reinforcement learning an information bottleneck approach"):
  with members:
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "reward constrained policy optimization"
    - "state representation learning for control an overview"
    - "addressing function approximation error in actor critic methods"
    - "mine mutual information neural estimation"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"

Connected through hyperedge HE-7 (induced by source paper "terminal prediction as an auxiliary task for deep reinforcement learning"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "backplay man muss immer umkehren"
    - "an introduction to deep reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "a deep learning approach for joint video frame and reward prediction in atari games"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "optimal farsighted agents tend to seek power"):
  with members:
    - "conservative agency via attainable utility preservation"
    - "asymptotically unambitious artificial general intelligence"
    - "the off switch game"
    - "empowerment an introduction"

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)