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 "low budget unsupervised label query through domain alignment enforcement"):
  Abstract: Deep learning revolution happened thanks to the availability of a massive amount of labelled data which have contributed to the development of models with extraordinary inference capabilities. Despite the public availability of a large quantity of datasets, it is often necessary to generate a new set of labelled data to address specific requirements. In addition, the production of labels is costly and sometimes it requires a specific expertise to be fulfilled. In this work, we introduce a new problem called low budget unsupervised label query that consists in a model trained to suggests to the user a set of samples to be labelled, from a completely unlabelled dataset, to maximize the classification accuracy on that dataset. We propose to adopt a domain alignment model, modified to enforce consistency, to align a known dataset (source) and the dataset to be labelled (target). Finally, we propose a novel sample selection method based on uniform entropy sampling, named UNFOLD, which is deterministic and steadily outperforms other baselines as well as competing models on a large variety of publicly available datasets.

Includes members:
  Member "self supervised learning with geometric constraints in monocular video connecting flow depth and camera":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "occlusion aware unsupervised learning of optical flow from video")
  Member "image difficulty curriculum for generative adversarial networks cugan":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "curriculum learning strategies for ir an empirical study on conversation response ranking")
  Member "unsupervised domain adaptation via regularized conditional alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "confidence regularized self training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross domain self supervised learning for domain adaptation with few source labels")
  Member "beyond sharing weights for deep domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "domain generalization by solving jigsaw puzzles":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain adaptive ensemble learning")
  Member "consistency based semi supervised active learning towards minimizing labeling budget":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parting with illusions about deep active learning")
  Member "self paced deep learning for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")

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)