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 "crdoco pixel level domain transfer with cross domain consistency"):
  Abstract: Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e.g., synthetic to real images). The adapted representations often do not capture pixel-level domain shifts that are crucial for dense prediction tasks (e.g., semantic segmentation). In this paper, we present a novel pixel-wise adversarial domain adaptation algorithm. By leveraging image-to-image translation methods for data augmentation, our key insight is that while the translated images between domains may differ in styles, their predictions for the task should be consistent. We exploit this property and introduce a cross-domain consistency loss that enforces our adapted model to produce consistent predictions. Through extensive experimental results, we show that our method compares favorably against the state-of-the-art on a wide variety of unsupervised domain adaptation tasks.

Includes members:
  Member "recover and identify a generative dual model for cross resolution person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "learning correspondence from the cycle consistency of time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "learning resolution invariant deep representations for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning shape representations for clothing variations in person re identification")
  Member "show match and segment joint learning of semantic matching and object co segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross dataset person re identification via unsupervised pose disentanglement and adaptation")
  Member "model adaptation with synthetic and real data for semantic dense foggy scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a little fog for a large turn")
  Member "learning to adapt structured output space for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "df net unsupervised joint learning of depth and flow using cross task consistency":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "deepmvs learning multi view stereopsis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")

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)