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 practical visual search engine within elasticsearch"
  Abstract: In this paper, we describe our end-to-end content-based image retrieval system built upon Elasticsearch, a well-known and popular textual search engine. As far as we know, this is the first time such a system has been implemented in eCommerce, and our efforts have turned out to be highly worthwhile. We end up with a novel and exciting visual search solution that is extremely easy to be deployed, distributed, scaled and monitored in a cost-friendly manner. Moreover, our platform is intrinsically flexible in supporting multimodal searches, where visual and textual information can be jointly leveraged in retrieval. #R##N#The core idea is to encode image feature vectors into a collection of string tokens in a way such that closer vectors will share more string tokens in common. By doing that, we can utilize Elasticsearch to efficiently retrieve similar images based on similarities within encoded sting tokens. As part of the development, we propose a novel vector to string encoding method, which is shown to substantially outperform the previous ones in terms of both precision and latency. #R##N#First-hand experiences in implementing this Elasticsearch-based platform are extensively addressed, which should be valuable to practitioners also interested in building visual search engine on top of Elasticsearch.

Connected through hyperedge HE-1 (induced by source paper "baod budget aware object detection"):
  with members:
    - "a flexible model for training action localization with varying levels of supervision"
    - "pcn part and context information for pedestrian detection with cnns"
    - "crowd counting by adaptively fusing predictions from an image pyramid"
    - "localization aware active learning for object detection"
    - "generative adversarial active learning"
    - "learning active learning from data"
    - "extreme clicking for efficient object annotation"
    - "deep self taught learning for weakly supervised object localization"

Connected through hyperedge HE-2 (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"
    - "billion scale similarity search with gpus"
    - "visual search at ebay"
    - "semantic vector encoding and similarity search using fulltext search engines"

Connected through hyperedge HE-3 (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"
    - "billion scale similarity search with gpus"
    - "visual search at ebay"
    - "semantic vector encoding and similarity search using fulltext search engines"
    - "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-4 (induced by source paper "end to end neural ranking for ecommerce product search an application of task models and textual embeddings"):
  with members:
    - "towards a simplified ontology for better e commerce search"
    - "modeling and simultaneously removing bias via adversarial neural networks"
    - "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.CV(Computer Vision and Pattern Recognition)