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: "diameter based active learning"
  Abstract: To date, the tightest upper and lower-bounds for the active learning of general concept classes have been in terms of a parameter of the learning problem called the splitting index. We provide, for the first time, an efficient algorithm that is able to realize this upper bound, and we empirically demonstrate its good performance.

Connected through hyperedge HE-1 (induced by source paper "adaptive region based active learning"):
  with members:
    - "efficient active learning of sparse halfspaces"
    - "on the relationship between data efficiency and error for uncertainty sampling"
    - "efficient learning of linear separators under bounded noise"
    - "efficient and parsimonious agnostic active learning"
    - "beyond disagreement based agnostic active learning"
    - "minimax analysis of active learning"
    - "active and passive learning of linear separators under log concave distributions"
    - "agnostic active learning without constraints"

Connected through hyperedge HE-2 (induced by source paper "diameter based interactive structure discovery"):
  with members:
    - "a general framework for robust interactive learning"
    - "interactive bayesian hierarchical clustering"
    - "equality of opportunity in supervised learning"
    - "clustering with same cluster queries"
    - "efficient learning of linear separators under bounded noise"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "local algorithms for interactive clustering"
    - "robust interactive learning"

Connected through hyperedge HE-3 (induced by source paper "what is the value of data on mathematical methods for data quality estimation"):
  with members:
    - "optimal data acquisition for statistical estimation"
    - "low cost learning via active data procurement"
    - "analysis of boolean functions"

Connected through hyperedge HE-4 (induced by source paper "flattening a hierarchical clustering through active learning"):
  with members:
    - "generalized binary search for split neighborly problems"
    - "near optimal bayesian active learning with correlated and noisy tests"
    - "clustering with same cluster queries"
    - "local algorithms for interactive clustering"
    - "efficient active learning of halfspaces an aggressive approach"
    - "agnostic active learning without constraints"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "importance weighted active learning"

Connected through hyperedge HE-5 (induced by source paper "a structured perspective of volumes on active learning"):
  with members:
    - "robust and discriminative labeling for multi label active learning based on maximum correntropy criterion"
    - "exploring representativeness and informativeness for active learning"
    - "learning algorithms for active learning"
    - "active discriminative text representation learning"
    - "efficient active learning of halfspaces an aggressive approach"

Connected through hyperedge HE-6 (induced by source paper "a divide and conquer approach to geometric sampling for active learning"):
  with members:
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "deep bayesian active learning with image data"

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)