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: "a brand level ranking system with the customized attention gru model"
  Abstract: In e-commerce websites like Taobao, brand is playing a more important role in influencing users' decision of click/purchase, partly because users are now attaching more importance to the quality of products and brand is an indicator of quality. However, existing ranking systems are not specifically designed to satisfy this kind of demand. Some design tricks may partially alleviate this problem, but still cannot provide satisfactory results or may create additional interaction cost. In this paper, we design the first brand-level ranking system to address this problem. The key challenge of this system is how to sufficiently exploit users' rich behavior in e-commerce websites to rank the brands. In our solution, we firstly conduct the feature engineering specifically tailored for the personalized brand ranking problem and then rank the brands by an adapted Attention-GRU model containing three important modifications. Note that our proposed modifications can also apply to many other machine learning models on various tasks. We conduct a series of experiments to evaluate the effectiveness of our proposed ranking model and test the response to the brand-level ranking system from real users on a large-scale e-commerce platform, i.e. Taobao.

Connected through hyperedge HE-1 (induced by source paper "towards neural mixture recommender for long range dependent user sequences"):
  with members:
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "a simple convolutional generative network for next item recommendation"
    - "top k off policy correction for a reinforce recommender system"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "session aware information embedding for e commerce product recommendation"
    - "contextual sequence modeling for recommendation with recurrent neural networks"

Connected through hyperedge HE-2 (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"
    - "learning a deep listwise context model for ranking refinement"
    - "automatic generation of chinese short product titles for mobile display"
    - "representing verbs as argument concepts"
    - "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-3 (induced by source paper "query based interactive recommendation by meta path and adapted attention gru"):
  with members:
    - "exact k recommendation via maximal clique optimization"
    - "addressing the item cold start problem by attribute driven active learning"
    - "wide deep learning for recommender systems"
    - "session based recommendations with recurrent neural networks"
    - "attention based models for speech recognition"
    - "recurrent models of visual attention"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "addressing the item cold start problem by attribute driven active learning"):
  with members:
    - "budget constrained item cold start handling in collaborative filtering recommenders via optimal design"
    - "learning to optimize via posterior sampling"
    - "a bayesian approach toward active learning for collaborative filtering"
    - "analysis of thompson sampling for the multi armed bandit problem"

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)