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: "should one compute the temporal difference fix point or minimize the bellman residual the unified oblique projection view"
  Abstract: We investigate projection methods, for evaluating a linear approximation of the value function of a policy in a Markov Decision Process context. We consider two popular approaches, the one-step Temporal Difference fix-point computation (TD(0)) and the Bellman Residual (BR) minimization. We describe examples, where each method outperforms the other. We highlight a simple relation between the objective function they minimize, and show that while BR enjoys a performance guarantee, TD(0) does not in general. We then propose a unified view in terms of oblique projections of the Bellman equation, which substantially simplifies and extends the characterization of (schoknecht,2002) and the recent analysis of (Yu & Bertsekas, 2008). Eventually, we describe some simulations that suggest that if the TD(0) solution is usually slightly better than the BR solution, its inherent numerical instability makes it very bad in some cases, and thus worse on average.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning via fenchel rockafellar duality"):
  with members:
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "minimax weight and q function learning for off policy evaluation"
    - "doubly robust bias reduction in infinite horizon off policy estimation"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "algaedice policy gradient from arbitrary experience"
    - "a divergence minimization perspective on imitation learning methods"
    - "imitation learning as f divergence minimization"

Connected through hyperedge HE-2 (induced by source paper "deep residual reinforcement learning"):
  with members:
    - "a kernel loss for solving the bellman equation"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "world models"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "addressing function approximation error in actor critic methods"
    - "model based value estimation for efficient model free reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "adaptive temporal difference learning for policy evaluation with per state uncertainty estimates"):
  with members:
    - "temporal difference learning with neural networks study of the leakage propagation problem"
    - "efficient exploration through bayesian deep q networks"
    - "deep exploration via bootstrapped dqn"
    - "adaptive lambda least squares temporal difference learning"
    - "a greedy approach to adapting the trace parameter for temporal difference learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "off policy learning with eligibility traces a survey"

Connected through hyperedge HE-4 (induced by source paper "conditions on features for temporal difference like methods to converge"):
  with members:
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "on convergence of emphatic temporal difference learning"

Connected through hyperedge HE-5 (induced by source paper "an online prediction algorithm for reinforcement learning with linear function approximation using cross entropy method"):
  with members:
    - "a cross entropy based optimization algorithm with global convergence guarantees"
    - "solving factored mdps with hybrid state and action variables"
    - "reinforcement learning a survey"

Connected through hyperedge HE-6 (induced by source paper "equivalence between policy gradients and soft q learning"):
  with members:
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "asynchronous methods for deep reinforcement learning"
    - "pgq combining policy gradient and q learning"
    - "taming the noise in reinforcement learning via soft updates"
    - "high dimensional continuous control using generalized advantage estimation"
    - "gradient estimation using stochastic computation graphs"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "on generalized bellman equations and temporal difference learning"):
  with members:
    - "multi step off policy learning without importance sampling ratios"
    - "some simulation results for emphatic temporal difference learning algorithms"
    - "on convergence of emphatic temporal difference learning"
    - "weak convergence properties of constrained emphatic temporal difference learning with constant and slowly diminishing stepsize"
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"

Connected through hyperedge HE-8 (induced by source paper "learning sparse representations in reinforcement learning with sparse coding"):
  with members:
    - "simple efficient and neural algorithms for sparse coding"
    - "greedy algorithms for sparse 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.AI(Artificial Intelligence)