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: "regression with n 1 by expert knowledge elicitation"
  Abstract: We consider regression under the "extremely small n large p" condition, where the number of samples n is so small compared to the dimensionality p that predictors cannot be estimated without prior knowledge. This setup occurs in personalized medicine, for instance, when predicting treatment outcomes for an individual patient based on noisy high-dimensional genomics data. A remaining source of information is expert knowledge, which has received relatively little attention in recent years. We formulate the inference problem of asking expert feedback on features on a budget, propose an elicitation strategy for a simple "small n" setting, and derive conditions under which the elicitation strategy is optimal. Experiments on simulated experts, both on synthetic and genomics data, demonstrate that the proposed strategy can drastically improve prediction accuracy.

Connected through hyperedge HE-1 (induced by source paper "improving genomics based predictions for precision medicine through active elicitation of expert knowledge"):
  with members:
    - "knowledge elicitation via sequential probabilistic inference for high dimensional prediction"
    - "interactive prior elicitation of feature similarities for small sample size prediction"
    - "interactive elicitation of knowledge on feature relevance improves predictions in small data sets"
    - "expectation propogation for the generative aspect model"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-2 (induced by source paper "knowledge elicitation via sequential probabilistic inference for high dimensional prediction"):
  with members:
    - "interactive prior elicitation of feature similarities for small sample size prediction"
    - "interactive elicitation of knowledge on feature relevance improves predictions in small data sets"
    - "preference elicitation for general random utility models"
    - "expectation propagation for approximate bayesian inference"
    - "expectation propogation for the generative aspect model"

Connected through hyperedge HE-3 (induced by source paper "interactive elicitation of knowledge on feature relevance improves predictions in small data sets"):
  with members:
    - "knowledge elicitation via sequential probabilistic inference for high dimensional prediction"
    - "interactive prior elicitation of feature similarities for small sample size prediction"
    - "why should i trust you explaining the predictions of any classifier"

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)