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: "reinforcement learning to rank in e commerce search engine formalization analysis and application"
  Abstract: In e-commerce platforms such as Amazon and TaoBao, ranking items in a search session is a typical multi-step decision-making problem. Learning to rank (LTR) methods have been widely applied to ranking problems. However, such methods often consider different ranking steps in a session to be independent, which conversely may be highly correlated to each other. For better utilizing the correlation between different ranking steps, in this paper, we propose to use reinforcement learning (RL) to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session. Firstly, we formally define the concept of search session Markov decision process (SSMDP) to formulate the multi-step ranking problem. Secondly, we analyze the property of SSMDP and theoretically prove the necessity of maximizing accumulative rewards. Lastly, we propose a novel policy gradient algorithm for learning an optimal ranking policy, which is able to deal with the problem of high reward variance and unbalanced reward distribution of an SSMDP. Experiments are conducted in simulation and TaoBao search engine. The results demonstrate that our algorithm performs much better than online LTR methods, with more than 40% and 30% growth of total transaction amount in the simulation and the real application, respectively.

Connected through hyperedge HE-1 (induced by source paper "online similarity learning with feedback for invoice line item matching"):
  with members:
    - "calculating the similarity between words and sentences using a lexical database and corpus statistics"
    - "universal sentence encoder"
    - "towards a simplified ontology for better e commerce search"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning applications"):
  with members:
    - "coloring big graphs with alphagozero"
    - "learning agile and dynamic motor skills for legged robots"
    - "reinforcement learning for slate based recommender systems a tractable decomposition and practical methodology"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "deep learning based recommender system a survey and new perspectives"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning for search recommendation and online advertising a survey by xiangyu zhao long xia jiliang tang and dawei yin with martin vesely as coordinator"):
  with members:
    - "model based reinforcement learning for whole chain recommendations"
    - "learning to coordinate multiple reinforcement learning agents for diverse query reformulation"
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "deep reinforcement learning for sponsored search real time bidding"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "a multi agent reinforcement learning method for impression allocation in online display advertising"
    - "learning to advertise with adaptive exposure via constrained two level reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "explainable product search with a dynamic relation embedding model"):
  with members:
    - "conversational product search based on negative feedback"
    - "leverage implicit feedback for context aware product search"
    - "a zero attention model for personalized product search"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "learning a deep listwise context model for ranking refinement"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "learning over knowledge base embeddings for recommendation"
    - "explainable recommendation via multi task learning in opinionated text data"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning for personalized search story recommendation"):
  with members:
    - "skinnerdb regret bounded query evaluation via reinforcement learning"
    - "reinforcement learning to optimize long term user engagement in recommender systems"
    - "model free control for distributed stream data processing using deep reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "reinforcement mechanism design for e commerce"
    - "real time bidding by reinforcement learning in display advertising"
    - "deep reinforcement learning an overview"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "a zero attention model for personalized product search"):
  with members:
    - "attentive long short term preference modeling for personalized product search"
    - "ensemble methods for personalized e commerce search challenge at cikm cup 2016"
    - "attention is all you need"
    - "an ensemble based approach to click through rate prediction for promoted listings at etsy"
    - "neural collaborative filtering"
    - "on application of learning to rank for e commerce search"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"

Connected through hyperedge HE-7 (induced by source paper "leverage implicit feedback for context aware product search"):
  with members:
    - "sequence aware recommender systems"
    - "learning a deep listwise context model for ranking refinement"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "session aware information embedding for e commerce product recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "on application of learning to rank for e commerce search"
    - "unbiased learning to rank with biased feedback"
    - "general factorization framework for context aware recommendations"

Connected through hyperedge HE-8 (induced by source paper "corpus level end to end exploration for interactive systems"):
  with members:
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "deeptilebars visualizing term distribution for neural information retrieval"
    - "a reinforcement learning driven translation model for search oriented conversational systems"
    - "coqa a conversational question answering challenge"
    - "modeling diverse relevance patterns in ad hoc retrieval"
    - "proximal policy optimization algorithms"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "visualizing and understanding neural models in nlp"

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)