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: "towards a simplified ontology for better e commerce search"
  Abstract: Query Understanding is a semantic search method that can classify tokens in a customer's search query to entities such as Product, Brand, etc. This method can overcome the limitations of bag-of-words methods but requires an ontology. We show that current ontologies are not optimized for search and propose a simplified ontology framework designed specifically for e-commerce search and retrieval. We also present three methods for automatically extracting product classes for the proposed ontology and compare their performance relative to each other.

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"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "universal sentence encoder"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-2 (induced by source paper "ranking sentences from product description bullets for better search"):
  with members:
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "text summarization techniques a brief survey"
    - "get to the point summarization with pointer generator networks"
    - "graph based neural multi document summarization"
    - "neural extractive summarization with side information"
    - "a deep reinforced model for abstractive summarization"
    - "deep reinforcement learning for dialogue generation"
    - "distraction based neural networks for document summarization"

Connected through hyperedge HE-3 (induced by source paper "end to end neural ranking for ecommerce product search an application of task models and textual embeddings"):
  with members:
    - "modeling and simultaneously removing bias via adversarial neural networks"
    - "towards practical visual search engine within elasticsearch"
    - "neural models for information retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

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)