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 "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"):
  Abstract: Despite significant progress of deep learning in recent years, state-of-the-art semantic matching methods still rely on legacy features such as SIFT or HoG. We argue that the strong invariance properties that are key to the success of recent deep architectures on the classification task make them unfit for dense correspondence tasks, unless a large amount of supervision is used. In this work, we propose a deep network, termed AnchorNet, that produces image representations that are well-suited for semantic matching. It relies on a set of filters whose response is geometrically consistent across different object instances, even in the presence of strong intra-class, scale, or viewpoint variations. Trained only with weak image-level labels, the final representation successfully captures information about the object structure and improves results of state-of-the-art semantic matching methods such as the deformable spatial pyramid or the proposal flow methods. We show positive results on the cross-instance matching task where different instances of the same object category are matched as well as on a new cross-category semantic matching task aligning pairs of instances each from a different object class.

Includes members:
  Member "dense correspondences across scenes and scales":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dense scale selection over space time and space time")
  Member "fully trainable deep matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pnp net a hybrid perspective n point network")
  Member "warpnet weakly supervised matching for single view reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised disentanglement of pose appearance and background from images and videos")
  Member "learning dense correspondence via 3d guided cycle consistency":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sim2real transfer learning for 3d pose estimation motion to the rescue")
  Member "vqa visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "dense semantic correspondence where every pixel is a classifier":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning semantic correspondence exploiting an object level prior")
  Member "stereo matching by training a convolutional neural network to compare image patches":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  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)