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: "active learning for cost sensitive classification"
  Abstract: We design an active learning algorithm for cost-sensitive multiclass classification: problems where different errors have different costs. Our algorithm, COAL, makes predictions by regressing to each label's cost and predicting the smallest. On a new example, it uses a set of regressors that perform well on past data to estimate possible costs for each label. It queries only the labels that could be the best, ignoring the sure losers. We prove COAL can be efficiently implemented for any regression family that admits squared loss optimization; it also enjoys strong guarantees with respect to predictive performance and labeling effort. We empirically compare COAL to passive learning and several active learning baselines, showing significant improvements in labeling effort and test cost on real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "deep active learning for remote sensing object detection"):
  with members:
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "yolov3 an incremental improvement"
    - "cost effective object detection active sample mining with switchable selection criteria"
    - "towards human machine cooperation self supervised sample mining for object detection"
    - "robust face detection via learning small faces on hard images"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "learning roi transformer for detecting oriented objects in aerial images"

Connected through hyperedge HE-2 (induced by source paper "regularized learning for domain adaptation under label shifts"):
  with members:
    - "detecting and correcting for label shift with black box predictors"
    - "reinforcement learning of pomdp s using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"
    - "mixture proportion estimation via kernel embedding of distributions"
    - "deep residual learning for image recognition"
    - "statistical linear estimation with penalized estimators an application to reinforcement learning"
    - "a method of moments for mixture models and hidden markov models"
    - "on causal and anticausal learning"

Connected through hyperedge HE-3 (induced by source paper "practical contextual bandits with regression oracles"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "scikit learn machine learning in python"
    - "contextual bandit learning with predictable rewards"
    - "the offset tree for learning with partial labels"

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)