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: "globally optimized mutual influence aware ranking in e commerce search"
  Abstract: In web search, mutual influences between documents have been studied from the perspective of search result diversification. But the methods in web search is not directly applicable to e-commerce search because of their differences. And little research has been done on the mutual influences between items in e-commerce search. We propose a global optimization framework for mutual influence aware ranking in e-commerce search. Our framework directly optimizes the Gross Merchandise Volume (GMV) for ranking, and decomposes ranking into two tasks. The first task is mutual influence aware purchase probability estimation. We propose a global feature extension method to incorporate mutual influences into the features of an item. We also use Recurrent Neural Network (RNN) to capture influences related to ranking orders in purchase probability estimation. The second task is to find the best ranking order based on the purchase probability estimations. We treat the second task as a sequence generation problem and solved it using the beam search algorithm. We performed online A/B test on a large e-commerce search engine. The results show that our method brings a 5% increase in GMV for the search engine over a strong baseline.

Connected through hyperedge HE-1 (induced by source paper "a domain generalization perspective on listwise context modeling"):
  with members:
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "modeling diverse relevance patterns in ad hoc retrieval"
    - "generalizing across domains via cross gradient training"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"
    - "learning to generalize meta learning for domain generalization"
    - "unified deep supervised domain adaptation and generalization"
    - "controllable invariance through adversarial feature learning"

Connected through hyperedge HE-2 (induced by source paper "aggregating e commerce search results from heterogeneous sources via hierarchical reinforcement learning"):
  with members:
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "ranking for relevance and display preferences in complex presentation layouts"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "personalized re ranking for recommendation"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "seq2slate re ranking and slate optimization with rnns"
    - "deep cross network for ad click predictions"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "wide deep learning for recommender systems"
    - "deep recurrent models with fast forward connections for neural machine translation"

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)