human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "semantic vector encoding and similarity search using fulltext search engines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an empirical comparison of faiss and fenshses for nearest neighbor search in hamming space")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "learning to hash for indexing big data a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "random vlad based deep hashing for efficient image retrieval")

Question: Please tell me which class the source paper 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)