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 "designovel s system description for fashion iq challenge 2019"):
  Abstract: This paper describes Designovel's systems which are submitted to the Fashion IQ Challenge 2019. Goal of the challenge is building an image retrieval system where input query is a candidate image plus two text phrases describe user's feedback about visual differences between the candidate image and the search target. We built the systems by combining methods from recent work on deep metric learning, multi-modal retrieval and natual language processing. First, we encode both candidate and target images with CNNs into high-level representations, and encode text descriptions to a single text vector using Transformer-based encoder. Then we compose candidate image vector and text representation into a single vector which is exptected to be biased toward target image vector. Finally, we compute cosine similarities between composed vector and encoded vectors of whole dataset, and rank them in desceding order to get ranked list. We experimented with Fashion IQ 2019 dataset in various settings of hyperparameters, achieved 39.12% average recall by a single model and 43.67% average recall by an ensemble of 16 models on test dataset.

Includes members:
  Member "ranked list loss for deep metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "the fashion iq dataset retrieving images by combining side information and relative natural language feedback":
    also connected through other hyperedges:
      (none)
  Member "mmdetection open mmlab detection toolbox and benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mixpath a unified approach for one shot neural architecture search")
  Member "composing text and image for image retrieval an empirical odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning cross modal context graph for visual grounding")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")

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.CV(Computer Vision and Pattern Recognition)