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: "committee based sample selection for probabilistic classifiers"
  Abstract: In many real-world learning tasks it is expensive to acquire a sufficient number of labeled examples for training. This paper investigates methods for reducing annotation cost by sample selection. In this approach, during training the learning program examines many unlabeled examples and selects for labeling only those that are most informative at each stage. This avoids redundantly labeling examples that contribute little new information.#R##N##R##N#Our work follows on previous research on Query By Committee, and extends the committee-based paradigm to the context of probabilistic classification. We describe a family of empirical methods for committee-based sample selection in probabilistic classification models, which evaluate the informativeness of an example by measuring the degree of disagreement between several model variants. These variants (the committee) are drawn randomly from a probability distribution conditioned by the training set labeled so far.#R##N##R##N#The method was applied to the real-world natural language processing task of stochastic part-of-speech tagging. We find that all variants of the method achieve a significant reduction in annotation cost, although their computational efficiency differs. In particular, the simplest variant, a two member committee with no parameters to tune, gives excellent results. We also show that sample selection yields a significant reduction in the size of the model used by the tagger.

Connected through hyperedge HE-1 (induced by source paper "consensus driven propagation in massive unlabeled data for face recognition"):
  with members:
    - "the devil of face recognition is in the noise"
    - "accelerated training for massive classification via dynamic class selection"
    - "arcface additive angular margin loss for deep face recognition"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"
    - "learning transferable architectures for scalable image recognition"
    - "mix and match tuning for self supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "active expansion sampling for learning feasible domains in an unbounded input space"):
  with members:
    - "near optimal active learning of halfspaces via query synthesis in the noisy setting"
    - "hierarchical subquery evaluation for active learning on a graph"
    - "exponentiated gradient exploration for active learning"

Connected through hyperedge HE-3 (induced by source paper "analysis of stopping active learning based on stabilizing predictions"):
  with members:
    - "bucking the trend large scale cost focused active learning for statistical machine translation"
    - "taking into account the differences between actively and passively acquired data the case of active learning with support vector machines for imbalanced datasets"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"
    - "active learning with statistical models"

Connected through hyperedge HE-4 (induced by source paper "confidence estimation in structured prediction"):
  with members:
    - "introduction to the conll 2003 shared task language independent named entity recognition"
    - "introduction to the conll 2002 shared task language independent named entity recognition"
    - "introduction to the conll 2000 shared task chunking"

Connected through hyperedge HE-5 (induced by source paper "active learning with multiple views"):
  with members:
    - "wrapper maintenance a machine learning approach"
    - "active learning with statistical models"

Connected through hyperedge HE-6 (induced by source paper "acquiring word meaning mappings for natural language interfaces"):
  with members:
    - "noun phrase co occurrence statistics for semi automatic semantic lexicon construction"
    - "inducing a semantically annotated lexicon via em based clustering"
    - "a probabilistic approach to lexical semantic knowledge acquisition and structural disambiguation"

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.AI(Artificial Intelligence)