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 "traffickcam explainable image matching for sex trafficking investigations"):
  Abstract: Investigations of sex trafficking sometimes have access to photographs of victims in hotel rooms. These images directly link victims to places, which can help verify where victims have been trafficked or where traffickers might operate in the future. Current machine learning approaches give promising results in image search to find the matching hotel. This paper explores approaches to make this end-to-end system better support government and law enforcement requirements, including improved performance, visualization approaches that explain what parts of the image led to a match, and infrastructure to support exporting the results of a query.

Includes members:
  Member "hotels 50k a global hotel recognition dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved embeddings with easy positive triplet mining")
  Member "improved embeddings with easy positive triplet mining":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adapting grad cam for embedding networks")
  Member "visualizing deep similarity networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adapting grad cam for embedding networks")
  Member "visual interpretability for deep learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "semi supervised learning for detecting human trafficking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")
  Member "combating human trafficking with deep multimodal models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hotels 50k a global hotel recognition dataset")
  Member "faceless person recognition privacy implications in social media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "person recognition in personal photo collections")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)