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: "multiple instance learning by discriminative training of markov networks"
  Abstract: We introduce a graphical framework for multiple instance learning (MIL) based on Markov networks. This framework can be used to model the traditional MIL definition as well as more general MIL definitions. Different levels of ambiguity -- the portion of positive instances in a bag -- can be explored in weakly supervised data. To train these models, we propose a discriminative max-margin learning algorithm leveraging efficient inference for cardinality-based cliques. The efficacy of the proposed framework is evaluated on a variety of data sets. Experimental results verify that encoding or learning the degree of ambiguity can improve classification performance.

Connected through hyperedge HE-1 (induced by source paper "learning to find common objects across few image collections"):
  with members:
    - "attention based deep multiple instance learning"
    - "prototypical networks for few shot learning"
    - "deep self taught learning for weakly supervised object localization"
    - "revisiting knowledge transfer for training object class detectors"
    - "few shot image recognition by predicting parameters from activations"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "rapid adaptation with conditionally shifted neurons"
    - "weakly supervised object localization using things and stuff transfer"

Connected through hyperedge HE-2 (induced by source paper "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"):
  with members:
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "recurrent convolutional neural network regression for continuous pain intensity estimation in video"
    - "multi instance dynamic ordinal random fields for weakly supervised pain intensity estimation"
    - "variable state latent conditional random fields for facial expression recognition and action unit detection"
    - "fast exact inference for recursive cardinality models"

Connected through hyperedge HE-3 (induced by source paper "multi instance dynamic ordinal random fields for weakly supervised pain intensity estimation"):
  with members:
    - "variable state latent conditional random fields for facial expression recognition and action unit detection"
    - "fast exact inference for recursive cardinality models"
    - "multi instance learning by treating instances as non i i d samples"

Connected through hyperedge HE-4 (induced by source paper "visual recognition by counting instances a multi instance cardinality potential kernel"):
  with members:
    - "weakly supervised action labeling in videos under ordering constraints"
    - "fast exact inference for recursive cardinality models"

Connected through hyperedge HE-5 (induced by source paper "learning to detect blue white structures in dermoscopy images with weak supervision"):
  with members:
    - "automatic detection of blue white veil and related structures in dermoscopy images"

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)