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: "weak convergence properties of constrained emphatic temporal difference learning with constant and slowly diminishing stepsize"
  Abstract: We consider the emphatic temporal-difference (TD) algorithm, ETD($\lambda$), for learning the value functions of stationary policies in a discounted, finite state and action Markov decision process. The ETD($\lambda$) algorithm was recently proposed by Sutton, Mahmood, and White to solve a long-standing divergence problem of the standard TD algorithm when it is applied to off-policy training, where data from an exploratory policy are used to evaluate other policies of interest. The almost sure convergence of ETD($\lambda$) has been proved in our recent work under general off-policy training conditions, but for a narrow range of diminishing stepsize. In this paper we present convergence results for constrained versions of ETD($\lambda$) with constant stepsize and with diminishing stepsize from a broad range. Our results characterize the asymptotic behavior of the trajectory of iterates produced by those algorithms, and are derived by combining key properties of ETD($\lambda$) with powerful convergence theorems from the weak convergence methods in stochastic approximation theory. For the case of constant stepsize, in addition to analyzing the behavior of the algorithms in the limit as the stepsize parameter approaches zero, we also analyze their behavior for a fixed stepsize and bound the deviations of their averaged iterates from the desired solution. These results are obtained by exploiting the weak Feller property of the Markov chains associated with the algorithms, and by using ergodic theorems for weak Feller Markov chains, in conjunction with the convergence results we get from the weak convergence methods. Besides ETD($\lambda$), our analysis also applies to the off-policy TD($\lambda$) algorithm, when the divergence issue is avoided by setting $\lambda$ sufficiently large.

Connected through hyperedge HE-1 (induced by source paper "online off policy prediction"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deep reinforcement learning that matters"
    - "multi step off policy learning without importance sampling ratios"
    - "stochastic variance reduction methods for policy evaluation"
    - "convergent tree backup and retrace with function approximation"
    - "on convergence of some gradient based temporal differences algorithms for off policy learning"
    - "sample efficient actor critic with experience replay"
    - "reinforcement learning with unsupervised auxiliary tasks"

Connected through hyperedge HE-2 (induced by source paper "multi step off policy learning without importance sampling ratios"):
  with members:
    - "q lambda with off policy corrections"
    - "adaptive lambda least squares temporal difference learning"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "on convergence of emphatic temporal difference learning"

Connected through hyperedge HE-3 (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"
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"
    - "should one compute the temporal difference fix point or minimize the bellman residual the unified oblique projection view"

Connected through hyperedge HE-4 (induced by source paper "on convergence of some gradient based temporal differences algorithms for off policy learning"):
  with members:
    - "multi step off policy learning without importance sampling ratios"
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"
    - "sparse q learning with mirror descent"

Connected through hyperedge HE-5 (induced by source paper "o 2 td near optimal off policy td learning"):
  with members:
    - "should one compute the temporal difference fix point or minimize the bellman residual the unified oblique projection view"

Connected through hyperedge HE-6 (induced by source paper "an emphatic approach to the problem of off policy temporal difference learning"):
  with members:
    - "emphatic temporal difference learning"
    - "true online emphatic td lambda quick reference and implementation guide"
    - "on convergence of emphatic temporal difference learning"
    - "off policy learning with eligibility traces a survey"
    - "multi timescale nexting in a reinforcement learning robot"

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)