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: "temporal difference networks"
  Abstract: We introduce a generalization of temporal-difference (TD) learning to networks of interrelated predictions. Rather than relating a single prediction to itself at a later time, as in conventional TD methods, a TD network relates each prediction in a set of predictions to other predictions in the set at a later time. TD networks can represent and apply TD learning to a much wider class of predictions than has previously been possible. Using a random-walk example, we show that these networks can be used to learn to predict by a fixed interval, which is not possible with conventional TD methods. Secondly, we show that if the inter-predictive relationships are made conditional on action, then the usual learning-efficiency advantage of TD methods over Monte Carlo (supervised learning) methods becomes particularly pronounced. Thirdly, we demonstrate that TD networks can learn predictive state representations that enable exact solution of a non-Markov problem. A very broad range of inter-predictive temporal relationships can be expressed in these networks. Overall we argue that TD networks represent a substantial extension of the abilities of TD methods and bring us closer to the goal of representing world knowledge in entirely predictive, grounded terms.

Connected through hyperedge HE-1 (induced by source paper "what s a good prediction issues in evaluating general value functions through error"):
  with members:
    - "learning feature relevance through step size adaptation in temporal difference learning"
    - "adapting behaviour via intrinsic reward a survey and empirical study"
    - "when is a prediction knowledge"
    - "general value function networks"
    - "a first empirical study of emphatic temporal difference learning"
    - "introspective agents confidence measures for general value functions"
    - "multi timescale nexting in a reinforcement learning robot"

Connected through hyperedge HE-2 (induced by source paper "discovery of useful questions as auxiliary tasks"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "meta descent for online continual prediction"):
  with members:
    - "on the convergence of adam and beyond"
    - "understanding short horizon bias in stochastic meta optimization"
    - "tidbd adapting temporal difference step sizes through stochastic meta descent"
    - "online learning rate adaptation with hypergradient descent"
    - "effective sketching methods for value function approximation"
    - "learning to learn by gradient descent by gradient descent"
    - "accelerated gradient temporal difference learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "improved reinforcement learning with curriculum"):
  with members:
    - "reverse curriculum generation for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "deep residual learning for image recognition"
    - "tdleaf lambda combining temporal difference learning with game tree search"

Connected through hyperedge HE-5 (induced by source paper "general value function networks"):
  with members:
    - "online off policy prediction"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "learning to filter with predictive state inference machines"
    - "learning to predict independent of span"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "temporal difference networks for dynamical systems with continuous observations and actions"
    - "predictive linear gaussian models of stochastic dynamical systems"

Connected through hyperedge HE-6 (induced by source paper "learning to make predictions in partially observable environments without a generative model"):
  with members:
    - "blind construction of optimal nonlinear recursive predictors for discrete sequences"
    - "the optimal reward baseline for gradient based reinforcement learning"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "predictive linear gaussian models of stochastic dynamical systems"
    - "closing the learning planning loop with predictive state representations"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-7 (induced by source paper "multi timescale nexting in a reinforcement learning robot"):
  with members:
    - "predictive state representations a new theory for modeling dynamical systems"
    - "scaling life long off policy learning"
    - "closing the learning planning loop with predictive state representations"

Connected through hyperedge HE-8 (induced by source paper "a monte carlo aixi approximation"):
  with members:
    - "predictive state representations a new theory for modeling dynamical systems"
    - "reinforcement learning via aixi approximation"
    - "closing the learning planning loop with predictive state representations"
    - "feature dynamic bayesian networks"
    - "online planning algorithms for pomdps"
    - "feature markov decision processes"
    - "universal reinforcement learning"
    - "universal algorithmic intelligence a mathematical top down approach"

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)