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 "class conditional domain adaptation on semantic segmentation"):
  Abstract: Semantic segmentation is an important sub-task for many applications, but pixel-level ground truth labeling is costly and there is a tendency to overfit the training data, limiting generalization. Unsupervised domain adaptation can potentially address these problems, allowing systems trained on labelled datasets from one or more source domains (including less expensive synthetic domains) to be adapted to novel target domains. The conventional approach is to automatically align the representational distributions of source and target domains. One limitation of this approach is that it tends to disadvantage lower probability classes. We address this problem by introducing a Class-Conditional Domain Adaptation method (CCDA). It includes a class-conditional multi-scale discriminator and the class-conditional loss. This novel CCDA method encourages the network to shift the domain in a class-conditional manner, and it equalizes loss over classes. We evaluate our CCDA method on two transfer tasks and demonstrate performance comparable to state-of-the-art methods.

Includes members:
  Member "domain adaptation for semantic segmentation with maximum squares loss":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differential treatment for stuff and things a simple unsupervised domain adaptation method for semantic segmentation")
  Member "bidirectional learning for domain adaptation of semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors")
  Member "domain adaptation for structured output via discriminative patch representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adapting object detectors with conditional domain normalization")
  Member "transferrable prototypical networks for unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "significance aware information bottleneck for domain adaptive semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "label driven reconstruction for domain adaptation in semantic segmentation")
  Member "fully convolutional adaptation networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "label driven reconstruction for domain adaptation in semantic segmentation")
  Member "penalizing top performers conservative loss for semantic segmentation adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors")
  Member "learning to adapt structured output space for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")

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)