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: "semantic vector encoding and similarity search using fulltext search engines"
  Abstract: Vector representations and vector space modeling (VSM) play a central role in modern machine learning. We propose a novel approach to `vector similarity searching' over dense semantic representations of words and documents that can be deployed on top of traditional inverted-index-based fulltext engines, taking advantage of their robustness, stability, scalability and ubiquity. #R##N#We show that this approach allows the indexing and querying of dense vectors in text domains. This opens up exciting avenues for major efficiency gains, along with simpler deployment, scaling and monitoring. #R##N#The end result is a fast and scalable vector database with a tunable trade-off between vector search performance and quality, backed by a standard fulltext engine such as Elasticsearch. #R##N#We empirically demonstrate its querying performance and quality by applying this solution to the task of semantic searching over a dense vector representation of the entire English Wikipedia.

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:
    - "empowering elasticsearch with exact and fast r neighbor search in hamming space"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "fast and exact nearest neighbor search in hamming space on full text search engines"):
  with members:
    - "an empirical comparison of faiss and fenshses for nearest neighbor search in hamming space"
    - "towards practical visual search engine within elasticsearch"
    - "billion scale similarity search with gpus"
    - "visual search at ebay"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "pass join a partition based method for similarity joins"

Connected through hyperedge HE-3 (induced by source paper "empowering elasticsearch with exact and fast r neighbor search in hamming space"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning to hash for indexing big data a survey"

Connected through hyperedge HE-4 (induced by source paper "towards practical visual search engine within elasticsearch"):
  with members:
    - "empowering elasticsearch with exact and fast r neighbor search in hamming space"
    - "convolutional hashing for automated scene matching"
    - "binary generative adversarial networks for image retrieval"
    - "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)