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: "exponentiated gradient exploration for active learning"
  Abstract: Active learning strategies respond to the costly labelling task in a supervised classification by selecting the most useful unlabelled examples in training a predictive model. Many conventional active learning algorithms focus on refining the decision boundary, rather than exploring new regions that can be more informative. In this setting, we propose a sequential algorithm named EG-Active that can improve any Active learning algorithm by an optimal random exploration. Experimental results show a statistically significant and appreciable improvement in the performance of our new approach over the existing active feedback methods.

Connected through hyperedge HE-1 (induced by source paper "knn and ann based recognition of handwritten pashto letters using zoning features"):
  with members:
    - "indic handwritten script identification using offline online multimodal deep network"
    - "drawing and recognizing chinese characters with recurrent neural network"

Connected through hyperedge HE-2 (induced by source paper "towards secure iot communication with smart contracts in a blockchain infrastructure"):
  with members:
    - "indic handwritten script identification using offline online multimodal deep network"
    - "hyperledger fabric a distributed operating system for permissioned blockchains"
    - "platibart a platform for transactive iot blockchain applications with repeatable testing"
    - "blockchain in internet of things challenges and solutions"

Connected through hyperedge HE-3 (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"
    - "committee based sample selection for probabilistic classifiers"

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)