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: "potential based shaping and q value initialization are equivalent"
  Abstract: Shaping has proven to be a powerful but precarious means of improving reinforcement learning performance. Ng, Harada, and Russell (1999) proposed the potential-based shaping algorithm for adding shaping rewards in a way that guarantees the learner will learn optimal behavior.#R##N##R##N#In this note, we prove certain similarities between this shaping algorithm and the initialization step required for several reinforcement learning algorithms. More specifically, we prove that a reinforcement learner with initial Q-values based on the shaping algorithm's potential function make the same updates throughout learning as a learner receiving potential-based shaping rewards. We further prove that under a broad category of policies, the behavior of these two learners are indistinguishable. The comparison provides intuition on the theoretical properties of the shaping algorithm as well as a suggestion for a simpler method for capturing the algorithm's benefit. In addition, the equivalence raises previously unaddressed issues concerning the efficiency of learning with potential-based shaping.

Connected through hyperedge HE-1 (induced by source paper "perceptual values from observation"):
  with members:
    - "unsupervised visuomotor control through distributional planning networks"
    - "imitating latent policies from observation"
    - "playing hard exploration games by watching youtube"
    - "zero shot visual imitation"
    - "few shot goal inference for visuomotor learning and planning"
    - "generative adversarial imitation from observation"
    - "quantifying generalization in reinforcement learning"
    - "behavioral cloning from observation"

Connected through hyperedge HE-2 (induced by source paper "policy poisoning in batch reinforcement learning and control"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "data poisoning attacks on stochastic bandits"
    - "online data poisoning attack"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "data poisoning attacks in contextual bandits"
    - "is feature selection secure against training data poisoning"
    - "data poisoning attacks against online learning"
    - "machine teaching for inverse reinforcement learning algorithms and applications"

Connected through hyperedge HE-3 (induced by source paper "potential based advice for stochastic policy learning"):
  with members:
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "reinforcement learning with deep energy based policies"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "biased aggregation rollout and enhanced policy improvement for reinforcement learning"):
  with members:
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "the bottleneck simulator a model based deep reinforcement learning approach"
    - "value iteration with options and state aggregation"
    - "discretized approximations for pomdp with average cost"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "maximum expected hitting cost of a markov decision process and informativeness of rewards"):
  with members:
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

Connected through hyperedge HE-6 (induced by source paper "reward advancement transforming policy under maximum causal entropy principle"):
  with members:
    - "imitating driver behavior with generative adversarial networks"
    - "guided cost learning deep inverse optimal control via policy optimization"

Connected through hyperedge HE-7 (induced by source paper "rudder return decomposition for delayed rewards"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "meta gradient reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "forward backward reinforcement learning"
    - "world models"
    - "sparse attentive backtracking temporal creditassignment through reminding"

Connected through hyperedge HE-8 (induced by source paper "learning existing social conventions via observationally augmented self play"):
  with members:
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "learning with opponent learning awareness"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deep q learning from demonstrations"
    - "consequentialist conditional cooperation in social dilemmas with imperfect information"

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)