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: "bayesian optimal active search and surveying"
  Abstract: We consider two active binary-classification problems with atypical objectives. In the first, active search, our goal is to actively uncover as many members of a given class as possible. In the second, active surveying, our goal is to actively query points to ultimately predict the proportion of a given class. Numerous real-world problems can be framed in these terms, and in either case typical model-based concerns such as generalization error are only of secondary importance. #R##N#We approach these problems via Bayesian decision theory; after choosing natural utility functions, we derive the optimal policies. We provide three contributions. In addition to introducing the active surveying problem, we extend previous work on active search in two ways. First, we prove a novel theoretical result, that less-myopic approximations to the optimal policy can outperform more-myopic approximations by any arbitrary degree. We then derive bounds that for certain models allow us to reduce (in practice dramatically) the exponential search space required by a naive implementation of the optimal policy, enabling further lookahead while still ensuring that optimal decisions are always made.

Connected through hyperedge HE-1 (induced by source paper "efficient rollout strategies for bayesian optimization"):
  with members:
    - "lookahead bayesian optimization via rollout guarantees and sequential rolling horizons"
    - "improving the expected improvement algorithm"
    - "portfolio allocation for bayesian optimization"

Connected through hyperedge HE-2 (induced by source paper "learning a spatial field in minimum time with a team of robots"):
  with members:
    - "information theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing"
    - "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "efficient informative sensing using multiple robots"

Connected through hyperedge HE-3 (induced by source paper "a cost effective framework for preference elicitation and aggregation"):
  with members:
    - "composite marginal likelihood methods for random utility models"
    - "preference elicitation for general random utility models"

Connected through hyperedge HE-4 (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"
    - "building bridges viewing active learning from the multi armed bandit lens"
    - "the power of local information in social networks"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "importance weighted active learning"

Connected through hyperedge HE-5 (induced by source paper "active search for high recall a non stationary extension of thompson sampling"):
  with members:
    - "multi armed bandit problem with known trend"

Connected through hyperedge HE-6 (induced by source paper "discovering valuable items from massive data"):
  with members:
    - "bandits with knapsacks"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "a contextual bandit approach to personalized news article recommendation"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-7 (induced by source paper "a probabilistic interpretation of sampling theory of graph signals"):
  with members:
    - "active semi supervised learning using sampling theory for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "localized iterative methods for interpolation in graph structured data"

Connected through hyperedge HE-8 (induced by source paper "incremental active opinion learning over a stream of opinionated documents"):
  with members:
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"

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)