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: "learning a deep listwise context model for ranking refinement"
  Abstract: Learning to rank has been intensively studied and widely applied in information retrieval. Typically, a global ranking function is learned from a set of labeled data, which can achieve good performance on average but may be suboptimal for individual queries by ignoring the fact that relevant documents for different queries may have different distributions in the feature space. Inspired by the idea of pseudo relevance feedback where top ranked documents, which we refer as the local ranking context, can provide important information about the query's characteristics, we propose to use the inherent feature distributions of the top results to learn a Deep Listwise Context Model that helps us fine tune the initial ranked list. Specifically, we employ a recurrent neural network to sequentially encode the top results using their feature vectors, learn a local context model and use it to re-rank the top results. There are three merits with our model: (1) Our model can capture the local ranking context based on the complex interactions between top results using a deep neural network; (2) Our model can be built upon existing learning-to-rank methods by directly using their extracted feature vectors; (3) Our model is trained with an attention-based loss function, which is more effective and efficient than many existing listwise methods. Experimental results show that the proposed model can significantly improve the state-of-the-art learning to rank methods on benchmark retrieval corpora.

Connected through hyperedge HE-1 (induced by source paper "listwise learning to rank by exploring unique ratings"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "tf ranking scalable tensorflow library for learning to rank"
    - "a deep look into neural ranking models for information retrieval"
    - "ranking for relevance and display preferences in complex presentation layouts"
    - "applying deep learning to airbnb search"
    - "novel approaches to accelerating the convergence rate of markov decision process for search result diversification"
    - "learning groupwise multivariate scoring functions using deep neural networks"
    - "unbiased lambdamart an unbiased pairwise learning to rank algorithm"

Connected through hyperedge HE-2 (induced by source paper "a deep look into neural ranking models for information retrieval"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "simple applications of bert for ad hoc document retrieval"
    - "semeval 2017 task 3 community question answering"
    - "user intent prediction in information seeking conversations"
    - "vitor learning to rank webpages based on visual features"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "cross domain regularization for neural ranking models using adversarial learning"

Connected through hyperedge HE-3 (induced by source paper "recsim a configurable simulation platform for recommender systems"):
  with members:
    - "reinforcement learning when all actions are not always available"
    - "advantage amplification in slowly evolving latent state environments"
    - "challenges of real world reinforcement learning"
    - "toward simulating environments in reinforcement learning based recommendations"
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "natural environment benchmarks for reinforcement learning"
    - "deep dyna q integrating planning for task completion dialogue policy 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 heterogeneous knowledge base embeddings for explainable recommendation"
    - "learning over knowledge base embeddings for recommendation"
    - "explainable recommendation via multi task learning in opinionated text data"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"

Connected through hyperedge HE-5 (induced by source paper "exact k recommendation via maximal clique optimization"):
  with members:
    - "a multi task selected learning approach for solving 3d flexible bin packing problem"
    - "query tracking for e commerce conversational search a machine comprehension perspective"
    - "automatic generation of chinese short product titles for mobile display"
    - "representing verbs as argument concepts"
    - "a brand level ranking system with the customized attention gru model"
    - "deep cascade multi task learning for slot filling in online shopping assistant"
    - "behavioral cloning from observation"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning for slate based recommender systems a tractable decomposition and practical methodology"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "reinforcement learning based recommender system using biclustering technique"
    - "top k off policy correction for a reinforce recommender system"
    - "seq2slate re ranking and slate optimization with rnns"
    - "a survey on multi task learning"

Connected through hyperedge HE-7 (induced by source paper "self attentive document interaction networks for permutation equivariant ranking"):
  with members:
    - "tf ranking scalable tensorflow library for learning to rank"
    - "a deep look into neural ranking models for information retrieval"
    - "attention based deep multiple instance learning"
    - "learning groupwise multivariate scoring functions using deep neural networks"
    - "deep sets"
    - "seq2slate re ranking and slate optimization with rnns"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "personalized context aware re ranking for e commerce recommender systems"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "seq2slate re ranking and slate optimization with rnns"
    - "self attentive sequential recommendation"
    - "deep cross network for ad click predictions"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "wide deep learning for recommender systems"

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.IR(Information Retrieval)