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: "building bridges viewing active learning from the multi armed bandit lens"
  Abstract: In this paper we propose a multi-armed bandit inspired, pool based active learning algorithm for the problem of binary classification. By carefully constructing an analogy between active learning and multi-armed bandits, we utilize ideas such as lower confidence bounds, and self-concordant regularization from the multi-armed bandit literature to design our proposed algorithm. Our algorithm is a sequential algorithm, which in each round assigns a sampling distribution on the pool, samples one point from this distribution, and queries the oracle for the label of this sampled point. The design of this sampling distribution is also inspired by the analogy between active learning and multi-armed bandits. We show how to derive lower confidence bounds required by our algorithm. Experimental comparisons to previously proposed active learning algorithms show superior performance on some standard UCI datasets.

Connected through hyperedge HE-1 (induced by source paper "a survey on practical applications of multi armed and contextual bandits"):
  with members:
    - "interpretable multi objective reinforcement learning through policy orchestration"
    - "low rank bandit methods for high dimensional dynamic pricing"
    - "context attentive bandits contextual bandit with restricted context"
    - "bandit models of human behavior reward processing in mental disorders"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "algorithm selection as a bandit problem with unbounded losses"

Connected through hyperedge HE-2 (induced by source paper "selective harvesting over networks"):
  with members:
    - "diversity promoting bayesian learning of latent variable models"
    - "analyzing the optimal neighborhood algorithms for budgeted and partial connected dominating set problems"
    - "the power of local information in social networks"
    - "bayesian optimal active search and surveying"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "importance weighted active learning"

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)