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 "needle in a haystack a framework for seeking small objects in big datasets"):
  Abstract: Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity --- adding new complexity to search tasks. Researchers working on Content-Based Image Retrieval (CBIR) have traditionally tuned their search algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the results of a search query should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. We propose a new framework for image retrieval that models object-level regions using image keypoints retrieved from an image index, which are then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method Needle-Haystack (NH) scoring, and it is optimized for fast matrix operations on CPUs. We show that this method not only performs comparably to state-of-the-art methods in classic CBIR problems, but also outperforms them in fine-grained object- and instance-level retrieval on the Oxford 5K, Paris 6K, Google-Landmarks, and NIST MFC2018 datasets, as well as meme-style imagery from Reddit.

Includes members:
  Member "discovering visual patterns in art collections with spatially consistent feature learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep convolutional embedding for digitized painting clustering")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "beyond pixels image provenance analysis leveraging metadata":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning transformation aware embeddings for image forensics")
  Member "heated up softmax embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "circle loss a unified perspective of pair similarity optimization")
  Member "spotting the difference context retrieval and analysis for improved forgery detection and localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic spatial verification for large scale object level image retrieval")
  Member "billion scale similarity search with gpus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "region based image retrieval revisited":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discriminative learning of open vocabulary object retrieval and localization by negative phrase augmentation")

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)