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: "robust and discriminative labeling for multi label active learning based on maximum correntropy criterion"
  Abstract: Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also hard to build a good model without diagnosing discriminative labels. Can we reduce the label costs and improve the ability to train a good model for multi-label learning simultaneously? #R##N#Active learning addresses the less training samples problem by querying the most valuable samples to achieve a better performance with little costs. In multi-label active learning, some researches have been done for querying the relevant labels with less training samples or querying all labels without diagnosing the discriminative information. They all cannot effectively handle the outlier labels for the measurement of uncertainty. Since Maximum Correntropy Criterion (MCC) provides a robust analysis for outliers in many machine learning and data mining algorithms, in this paper, we derive a robust multi-label active learning algorithm based on MCC by merging uncertainty and representativeness, and propose an efficient alternating optimization method to solve it. With MCC, our method can eliminate the influence of outlier labels that are not discriminative to measure the uncertainty. To make further improvement on the ability of information measurement, we merge uncertainty and representativeness with the prediction labels of unknown data. It can not only enhance the uncertainty but also improve the similarity measurement of multi-label data with labels information. Experiments on benchmark multi-label data sets have shown a superior performance than the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "three stream fusion network for first person interaction recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "non linear temporal subspace representations for activity recognition"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "boosted multiple kernel learning for first person activity recognition"
    - "attentional pooling for action recognition"
    - "convolutional long short term memory networks for recognizing first person interactions"
    - "action recognition with coarse to fine deep feature integration and asynchronous fusion"
    - "going deeper into first person activity recognition"

Connected through hyperedge HE-2 (induced by source paper "embedding visual hierarchy with deep networks for large scale visual recognition"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "yolo9000 better faster stronger"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"
    - "theano based large scale visual recognition with multiple gpus"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "a structured perspective of volumes on active learning"):
  with members:
    - "exploring representativeness and informativeness for active learning"
    - "diameter based active learning"
    - "learning algorithms for active learning"
    - "active discriminative text representation learning"
    - "efficient active learning of halfspaces an aggressive approach"

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)