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: "coresets for kernel regression"
  Abstract: Kernel regression is an essential and ubiquitous tool for non-parametric data analysis, particularly popular among time series and spatial data. However, the central operation which is performed many times, evaluating a kernel on the data set, takes linear time. This is impractical for modern large data sets. #R##N#In this paper we describe coresets for kernel regression: compressed data sets which can be used as proxy for the original data and have provably bounded worst case error. The size of the coresets are independent of the raw number of data points, rather they only depend on the error guarantee, and in some cases the size of domain and amount of smoothing. We evaluate our methods on very large time series and spatial data, and demonstrate that they incur negligible error, can be constructed extremely efficiently, and allow for great computational gains.

Connected through hyperedge HE-1 (induced by source paper "finding the mode of a kernel density estimate"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "optimal terminal dimensionality reduction in euclidean space"
    - "improved coresets for kernel density estimates"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "estimating continuous distributions in bayesian classifiers"
    - "union of random minkowski sums and network vulnerability analysis"
    - "geometric inference on kernel density estimates"

Connected through hyperedge HE-2 (induced by source paper "improved algorithms for time decay streams"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "nearly optimal distinct elements and heavy hitters on sliding windows"
    - "numerical linear algebra in the sliding window model"
    - "online facility location on semi random streams"
    - "new frameworks for offline and streaming coreset constructions"
    - "randomized composable core sets for distributed submodular maximization"
    - "frequent directions simple and deterministic matrix sketching"
    - "a new framework for distributed submodular maximization"

Connected through hyperedge HE-3 (induced by source paper "coresets for minimum enclosing balls over sliding windows"):
  with members:
    - "efficient representative subset selection over sliding windows"
    - "near optimal coresets of kernel density estimates"
    - "real time influence maximization on dynamic social streams"
    - "submodular optimization over sliding windows"
    - "streamed learning one pass svms"

Connected through hyperedge HE-4 (induced by source paper "hashing based estimators for kernel density in high dimensions"):
  with members:
    - "provably useful kernel matrix approximation in linear time"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "recursive sampling for the nystr om method"
    - "sample optimal density estimation in nearly linear time"
    - "optimal data dependent hashing for approximate near neighbors"

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)