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: "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"
  Abstract: Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforcement learning (e.g. maximizing evaluation signal which indicates cumulative reward from survival rates). However, none of these studies have considered to combine the benefits of supervised learning and reinforcement learning. In this paper, we propose Supervised Reinforcement Learning with Recurrent Neural Network (SRL-RNN), which fuses them into a synergistic learning framework. Specifically, SRL-RNN applies an off-policy actor-critic framework to handle complex relations among multiple medications, diseases and individual characteristics. The "actor" in the framework is adjusted by both the indicator signal and evaluation signal to ensure effective prescription and low mortality. RNN is further utilized to solve the Partially-Observed Markov Decision Process (POMDP) problem due to the lack of fully observed states in real world applications. Experiments on the publicly real-world dataset, i.e., MIMIC-3, illustrate that our model can reduce the estimated mortality, while providing promising accuracy in matching doctors' prescriptions.

Connected through hyperedge HE-1 (induced by source paper "developing multi task recommendations with long term rewards via policy distilled reinforcement learning"):
  with members:
    - "micro and macro level churn analysis of large scale mobile games"
    - "joint neural collaborative filtering for recommender systems"
    - "value aware recommendation based on reinforced profit maximization in e commerce systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "ranking for relevance and display preferences in complex presentation layouts"

Connected through hyperedge HE-2 (induced by source paper "transformer hawkes process"):
  with members:
    - "learning temporal point processes via reinforcement learning"
    - "self attention with relative position representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "comparative study of cnn and rnn for natural language processing"
    - "convolutional sequence to sequence learning"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "wasserstein learning of deep generative point process models"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning in healthcare a survey"):
  with members:
    - "does the artificial intelligence clinician learn optimal treatment strategies for sepsis in intensive care"
    - "a dual mode adaptive basal bolus advisor based on reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "improving sepsis treatment strategies by combining deep and kernel based reinforcement learning"
    - "partial policy based reinforcement learning for anatomical landmark localization in 3d medical images"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "automatic view planning with multi scale deep reinforcement learning agents"
    - "deep reinforcement learning for dynamic treatment regimes on medical registry data"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for clinical decision support a brief survey"):
  with members:
    - "asynchronous advantage actor critic agent for starcraft ii"
    - "deep variational reinforcement learning for pomdps"
    - "deep reinforcement learning a brief survey"
    - "continuous state space models for optimal sepsis treatment a deep reinforcement learning approach"
    - "a reinforcement learning approach to weaning of mechanical ventilation in intensive care units"
    - "reinforcement learning with deep energy based policies"
    - "asynchronous methods for deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-5 (induced by source paper "dynamic measurement scheduling for event forecasting using deep rl"):
  with members:
    - "representation balancing mdps for off policy policy evaluation"
    - "xgems generating examplars to explain black box models"
    - "an optimal policy for patient laboratory tests in intensive care units"
    - "explaining image classifiers by counterfactual generation"
    - "confounding robust policy improvement"
    - "continuous state space models for optimal sepsis treatment a deep reinforcement learning approach"
    - "why pay more when you can pay less a joint learning framework for active feature acquisition and classification"
    - "representation and reinforcement learning for personalized glycemic control in septic patients"

Connected through hyperedge HE-6 (induced by source paper "medi care ai predicting medications from billing codes via robust recurrent neural networks"):
  with members:
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "recurrent dropout without memory loss"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "recurrent neural networks for multivariate time series with missing values"
    - "doctor ai predicting clinical events via recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "clinical xlnet modeling sequential clinical notes and predicting prolonged mechanical ventilation"):
  with members:
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "publicly available clinical bert embeddings"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "towards the creation of a large corpus of synthetically identified clinical notes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "exploiting convolutional neural network for risk prediction with medical feature embedding"

Connected through hyperedge HE-8 (induced by source paper "exploiting cognitive structure for adaptive learning"):
  with members:
    - "transcribing content from structural images with spotlight mechanism"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "improving mild cognitive impairment prediction via reinforcement learning and dialogue simulation"
    - "deep reinforcement learning a brief survey"
    - "an actor critic algorithm for sequence prediction"
    - "session based recommendations with recurrent neural networks"
    - "on the difficulty of training recurrent neural networks"

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)