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: "beyond supervised classification extreme minimal supervision with the graph 1 laplacian"
  Abstract: We consider the task of classifying when an extremely reduced amount of labelled data is available. This problem is of a great interest, in several real-world problems, as obtaining large amounts of labelled data is expensive and time consuming. We present a novel semi-supervised framework for multi-class classification that is based on the normalised and non-smooth graph 1-Laplacian. Our transductive framework is framed under a novel functional with carefully selected class priors - that enforces a sufficiently smooth solution that strengthens the intrinsic relation between the labelled and unlabelled data. We demonstrate through extensive experimental results on large datasets CIFAR-10 and ChestX-ray14, that our method outperforms classic methods and readily competes with recent deep-learning approaches.

Connected through hyperedge HE-1 (induced by source paper "two cycle learning clustering based regularisation for deep semi supervised classification"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "semi supervised learning by label gradient alignment"
    - "superpixel contracted graph based learning for hyperspectral image classification"
    - "label propagation for deep semi supervised learning"
    - "deep clustering for unsupervised learning of visual features"
    - "deep metric transfer for label propagation with limited annotated data"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "adversarial dropout for supervised and semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "on defending against label flipping attacks on malware detection systems"):
  with members:
    - "can machine learning model with static features be fooled an adversarial machine learning approach"
    - "label propagation for deep semi supervised learning"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "is feature selection secure against training data poisoning"
    - "learning to reweight examples for robust deep learning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "data poisoning attacks against online learning"
    - "enhancing robustness of machine learning systems via data transformations"

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)