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: "average case active learning with costs"
  Abstract: We analyze the expected cost of a greedy active learning algorithm. Our analysis extends previous work to a more general setting in which different queries have different costs. Moreover, queries may have more than two possible responses and the distribution over hypotheses may be non uniform. Specific applications include active learning with label costs, active learning for multiclass and partial label queries, and batch mode active learning. We also discuss an approximate version of interest when there are very many queries.

Connected through hyperedge HE-1 (induced by source paper "a tight analysis of greedy yields subexponential time approximation for uniform decision tree"):
  with members:
    - "generalized binary search for split neighborly problems"
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "inapproximability of vc dimension and littlestone s dimension"
    - "inapproximability of nash equilibrium"
    - "efficient test selection in active diagnosis via entropy approximation"
    - "noisy bayesian active learning"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "structural query by committee"):
  with members:
    - "clustering with same cluster queries"

Connected through hyperedge HE-3 (induced by source paper "approximation algorithms for optimal decision trees and adaptive tsp problems"):
  with members:
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "information acquisition and exploitation in multichannel wireless networks"

Connected through hyperedge HE-4 (induced by source paper "diameter based active learning"):
  with members:
    - "active and passive learning of linear separators under log concave distributions"
    - "near optimal bayesian active learning with noisy observations"
    - "importance weighted active learning"

Connected through hyperedge HE-5 (induced by source paper "adaptive submodular ranking"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "scenario submodular cover"
    - "near optimal bayesian active learning for decision making"
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"
    - "analytical approach to parallel repetition"
    - "a note on the generalized min sum set cover problem"
    - "near optimal bayesian active learning with noisy observations"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-6 (induced by source paper "trading off worst and expected cost in decision tree problems and a value dependent model"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "near optimal bayesian active learning with noisy observations"
    - "interactive submodular set cover"
    - "types of cost in inductive concept learning"

Connected through hyperedge HE-7 (induced by source paper "decision trees for function evaluation simultaneous optimization of worst and expected cost"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "approximation algorithms for stochastic boolean function evaluation and stochastic submodular set cover"
    - "evaluation of dnf formulas"
    - "near optimal bayesian active learning with noisy observations"
    - "interactive submodular set cover"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "adaptive submodularity theory and applications in active learning and stochastic optimization"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "near optimal nonmyopic value of information in graphical models"
    - "adaptive submodular optimization under matroid constraints"
    - "near optimal bayesian active learning with noisy observations"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"
    - "interactive submodular set cover"
    - "optimal value of information in graphical models"
    - "online planning algorithms for pomdps"

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)