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: "on convergence of emphatic temporal difference learning"
  Abstract: We consider emphatic temporal-difference learning algorithms for policy evaluation in discounted Markov decision processes with finite spaces. Such algorithms were recently proposed by Sutton, Mahmood, and White (2015) as an improved solution to the problem of divergence of off-policy temporal-difference learning with linear function approximation. We present in this paper the first convergence proofs for two emphatic algorithms, ETD($\lambda$) and ELSTD($\lambda$). We prove, under general off-policy conditions, the convergence in $L^1$ for ELSTD($\lambda$) iterates, and the almost sure convergence of the approximate value functions calculated by both algorithms using a single infinitely long trajectory. Our analysis involves new techniques with applications beyond emphatic algorithms leading, for example, to the first proof that standard TD($\lambda$) also converges under off-policy training for $\lambda$ sufficiently large.

Connected through hyperedge HE-1 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation"):
  with members:
    - "gradientdice rethinking generalized offline estimation of stationary values"
    - "a nonparametric offpolicy policy gradient"
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "algaedice policy gradient from arbitrary experience"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "breaking the curse of horizon infinite horizon off policy estimation"

Connected through hyperedge HE-2 (induced by source paper "concentration bounds for temporal difference learning with linear function approximation the case of batch data and uniform sampling"):
  with members:
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "finite sample analyses for td 0 with function approximation"
    - "fast lstd using stochastic approximation finite time analysis and application to traffic control"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "provably convergent off policy actor critic with function approximation"):
  with members:
    - "minimax weight and q function learning for off policy evaluation"
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "convergent actor critic algorithms under off policy training and function approximation"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-5 (induced by source paper "a multi agent off policy actor critic algorithm for distributed reinforcement learning"):
  with members:
    - "distributed off policy actor critic reinforcement learning with policy consensus"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "convergent actor critic algorithms under off policy training and function approximation"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"
    - "q prop sample efficient policy gradient with an off policy critic"
    - "safe and efficient off policy reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "expected sarsa λ with control variate for variance reduction"):
  with members:
    - "per decision multi step temporal difference learning with control variates"
    - "finite sample analysis of the gtd policy evaluation algorithms in markov setting"
    - "finite sample analysis of two timescale stochastic approximation with applications to reinforcement learning"
    - "multi step off policy learning without importance sampling ratios"
    - "finite sample analyses for td 0 with function approximation"
    - "convergent tree backup and retrace with function approximation"
    - "investigating practical linear temporal difference learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"

Connected through hyperedge HE-7 (induced by source paper "decentralized multi agent reinforcement learning with networked agents recent advances"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "a communication efficient multi agent actor critic algorithm for distributed reinforcement learning"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "distributed policy evaluation under multiple behavior strategies"

Connected through hyperedge HE-8 (induced by source paper "a convergent off policy temporal difference algorithm"):
  with members:
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "online off policy prediction"
    - "a first empirical study of emphatic temporal difference learning"
    - "an emphatic approach to the problem of off policy temporal difference 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.LG(Machine Learning)