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: "empowering elasticsearch with exact and fast r neighbor search in hamming space"
  Abstract: A growing interest has been witnessed recently in building nearest neighbor search solutions within Elasticsearch--one of the most popular full-text search engines. In this paper, we focus specifically on Hamming space nearest neighbor search using Elasticsearch. By combining three techniques: bit operation, substring filtering and data preprocessing with permutation, we develop a novel approach called FENSHSES (Fast Exact Neighbor Search in Hamming Space on Elasticsearch), which achieves dramatic speed-ups over the existing term match baseline. This will empower Elasticsearch with the capability of fast information retrieval even when documents (e.g., texts, images and sounds) are represented with binary codes--a common practice in nowadays semantic representation learning.

Connected through hyperedge HE-1 (induced by source paper "an empirical comparison of faiss and fenshses for nearest neighbor search in hamming space"):
  with members:
    - "fast and exact nearest neighbor search in hamming space on full text search engines"
    - "a smart system for selection of optimal product images in e commerce"
    - "towards practical visual search engine within elasticsearch"
    - "billion scale similarity search with gpus"
    - "visual search at ebay"
    - "semantic vector encoding and similarity search using fulltext search engines"

Connected through hyperedge HE-2 (induced by source paper "towards practical visual search engine within elasticsearch"):
  with members:
    - "convolutional hashing for automated scene matching"
    - "binary generative adversarial networks for image retrieval"
    - "semantic vector encoding and similarity search using fulltext search engines"
    - "neural models for information retrieval"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "how transferable are features in deep neural networks"
    - "cnn features off the shelf an astounding baseline for recognition"

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)