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 "attending to discriminative certainty for domain adaptation"):
  Abstract: In this paper, we aim to solve for unsupervised domain adaptation of classifiers where we have access to label information for the source domain while these are not available for a target domain. While various methods have been proposed for solving these including adversarial discriminator based methods, most approaches have focused on the entire image based domain adaptation. In an image, there would be regions that can be adapted better, for instance, the foreground object may be similar in nature. To obtain such regions, we propose methods that consider the probabilistic certainty estimate of various regions and specify focus on these during classification for adaptation. We observe that just by incorporating the probabilistic certainty of the discriminator while training the classifier, we are able to obtain state of the art results on various datasets as compared against all the recent methods. We provide a thorough empirical analysis of the method by providing ablation analysis, statistical significance test, and visualization of the attention maps and t-SNE embeddings. These evaluations convincingly demonstrate the effectiveness of the proposed approach.

Includes members:
  Member "adversarial adaptation of scene graph models for understanding civic issues":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "looking back at labels a class based domain adaptation technique")
  Member "looking back at labels a class based domain adaptation technique":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dual adversarial domain adaptation")
  Member "multimodal differential network for visual question generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "deep visual domain adaptation a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "differential attention for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust explanations for visual question answering")
  Member "partial adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "multi adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "learning semantic sentence embeddings using sequential pair wise discriminator":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "uncertainty based class activation maps for visual question answering")

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)