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: "improving policy gradient by exploring under appreciated rewards"
  Abstract: This paper presents a novel form of policy gradient for model-free reinforcement learning (RL) with improved exploration properties. Current policy-based methods use entropy regularization to encourage undirected exploration of the reward landscape, which is ineffective in high dimensional spaces with sparse rewards. We propose a more directed exploration strategy that promotes exploration of under-appreciated reward regions. An action sequence is considered under-appreciated if its log-probability under the current policy under-estimates its resulting reward. The proposed exploration strategy is easy to implement, requiring small modifications to an implementation of the REINFORCE algorithm. We evaluate the approach on a set of algorithmic tasks that have long challenged RL methods. Our approach reduces hyper-parameter sensitivity and demonstrates significant improvements over baseline methods. Our algorithm successfully solves a benchmark multi-digit addition task and generalizes to long sequences. This is, to our knowledge, the first time that a pure RL method has solved addition using only reward feedback.

Connected through hyperedge HE-1 (induced by source paper "gaussian process policy optimization"):
  with members:
    - "evolved policy gradients"
    - "parameter space noise for exploration"
    - "curiosity driven exploration by self supervised prediction"
    - "proximal policy optimization algorithms"
    - "trust region policy optimization"
    - "gaussian processes for big data"

Connected through hyperedge HE-2 (induced by source paper "learning to generalize from sparse and underspecified rewards"):
  with members:
    - "self supervised generalisation with meta auxiliary learning"
    - "reward shaping via meta learning"
    - "on first order meta learning algorithms"
    - "multi task maximum entropy inverse reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "discourse aware neural rewards for coherent text generation"
    - "babyai first steps towards grounded language learning with a human in the loop"

Connected through hyperedge HE-3 (induced by source paper "maximum entropy regularized multi goal reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "visual reinforcement learning with imagined goals"
    - "curiosity driven experience prioritization via density estimation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "latent space policies for hierarchical reinforcement learning"
    - "energy based hindsight experience prioritization"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-4 (induced by source paper "alphax exploring neural architectures with deep neural networks and monte carlo tree search"):
  with members:
    - "nas bench 101 towards reproducible neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "practical deep learning architecture optimization"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "darts differentiable architecture search"

Connected through hyperedge HE-5 (induced by source paper "learning to reach goals without reinforcement learning"):
  with members:
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "distributional reinforcement learning for efficient exploration"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "learning latent plans from play"
    - "relay policy learning solving long horizon tasks via imitation and reinforcement learning"
    - "goal conditioned imitation learning"
    - "temporal difference models model free deep rl for model based control"

Connected through hyperedge HE-6 (induced by source paper "countering the effects of lead bias in news summarization via multi stage training and auxiliary losses"):
  with members:
    - "fine tune bert for extractive summarization"
    - "information asymmetry in kl regularized rl"
    - "content selection in deep learning models of summarization"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "banditsum extractive summarization as a contextual bandit"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"

Connected through hyperedge HE-7 (induced by source paper "on hard exploration for reinforcement learning a case study in pommerman"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "continual match based training in pommerman technical report"
    - "model free model based and general intelligence"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "backplay man muss immer umkehren"
    - "using monte carlo tree search as a demonstrator within asynchronous deep rl"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "human level intelligence or animal like abilities"

Connected through hyperedge HE-8 (induced by source paper "neural architecture search using deep neural networks and monte carlo tree search"):
  with members:
    - "nas bench 101 towards reproducible neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "practical deep learning architecture optimization"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"

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)