human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "consistency based semi supervised active learning towards minimizing labeling budget"
  Abstract: Active learning (AL) integrates data labeling and model training to minimize the labeling cost by prioritizing the selection of high value data that can best improve model performance. Readily-available unlabeled data are used for selection mechanisms, but are not used for model training in most conventional pool-based AL methods. To minimize the labeling cost, we unify unlabeled sample selection and model training based on two principles. First, we exploit both labeled and unlabeled data using semi-supervised learning (SSL) to distill information from unlabeled data that improves representation learning and sample selection. Second, we propose a simple yet effective selection metric that is coherent with the training objective such that the selected samples are effective at improving model performance. Experimental results demonstrate superior performance of our proposed principles for limited labeled data compared to alternative AL and SSL combinations. In addition, we study an important problem -- "When can we start AL?". We propose a measure that is empirically correlated with the AL target loss and can be used to assist in determining the proper start point.

Connected through hyperedge HE-1 (induced by source paper "low budget unsupervised label query through domain alignment enforcement"):
  with members:
    - "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "confidence regularized self training"
    - "beyond sharing weights for deep domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "self paced deep learning for weakly supervised object detection"
    - "unsupervised domain adaptation through self supervision"

Connected through hyperedge HE-2 (induced by source paper "parting with illusions about deep active learning"):
  with members:
    - "semi supervised semantic segmentation needs strong high dimensional perturbations"
    - "semi supervised semantic segmentation with high and low level consistency"
    - "rethinking deep active learning using unlabeled data at model training"
    - "variational adversarial active learning"
    - "unsupervised data augmentation for consistency training"
    - "learning loss for active learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "consistency regularization and cutmix for semi supervised semantic segmentation"

Question: Please tell me which class the center node 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.LG(Machine Learning)