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: "semi supervised semantic segmentation needs strong high dimensional perturbations"
  Abstract: Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption -- under which the data distribution consists of uniform class clusters of samples separated by low density regions -- as key to its success. We analyze the problem of semantic segmentation and find that the data distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem. We then identify the conditions that allow consistency regularization to work even without such low-density regions. This allows us to generalize the recently proposed CutMix augmentation technique to a powerful masked variant, CowMix, leading to a successful application of consistency regularization in the semi-supervised semantic segmentation setting and reaching state-of-the-art results in several standard datasets.

Connected through hyperedge HE-1 (induced by source paper "parting with illusions about deep active learning"):
  with members:
    - "semi supervised semantic segmentation with high and low level consistency"
    - "rethinking deep active learning using unlabeled data at model training"
    - "variational adversarial active learning"
    - "consistency based semi supervised active learning towards minimizing labeling budget"
    - "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.CV(Computer Vision and Pattern Recognition)