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: "diversifying sparsity using variational determinantal point processes"
  Abstract: We propose a novel diverse feature selection method based on determinantal point processes (DPPs). Our model enables one to flexibly define diversity based on the covariance of features (similar to orthogonal matching pursuit) or alternatively based on side information. We introduce our approach in the context of Bayesian sparse regression, employing a DPP as a variational approximation to the true spike and slab posterior distribution. We subsequently show how this variational DPP approximation generalizes and extends mean-field approximation, and can be learned efficiently by exploiting the fast sampling properties of DPPs. Our motivating application comes from bioinformatics, where we aim to identify a diverse set of genes whose expression profiles predict a tumor type where the diversity is defined with respect to a gene-gene interaction network. We also explore an application in spatial statistics. In both cases, we demonstrate that the proposed method yields significantly more diverse feature sets than classic sparse methods, without compromising accuracy.

Connected through hyperedge HE-1 (induced by source paper "composable core sets for determinant maximization a simple near optimal algorithm"):
  with members:
    - "composable core sets for determinant maximization problems via spectral spanners"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "subdeterminant maximization via nonconvex relaxations and anti concentration"
    - "how to be fair and diverse"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"
    - "streaming algorithms for submodular function maximization"

Connected through hyperedge HE-2 (induced by source paper "query focused video summarization dataset evaluation and a memory network based approach"):
  with members:
    - "summary transfer exemplar based subset selection for video summarization"
    - "kronecker determinantal point processes"
    - "video summarization with long short term memory"
    - "dynamic memory networks for visual and textual question answering"
    - "query focused extractive video summarization"
    - "fixed point algorithms for learning determinantal point processes"
    - "predicting important objects for egocentric video summarization"
    - "microsoft coco captions data collection and evaluation server"

Connected through hyperedge HE-3 (induced by source paper "kronecker determinantal point processes"):
  with members:
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "fixed point algorithms for learning determinantal point processes"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "efficient sampling for k determinantal point processes"
    - "learning mixtures of submodular shells with application to document summarization"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-4 (induced by source paper "efficient sampling for k determinantal point processes"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "notes on using determinantal point processes for clustering with applications to text clustering"
    - "determinantal clustering processes a nonparametric bayesian approach to kernel based semi supervised clustering"
    - "learning determinantal point processes"
    - "markov determinantal point processes"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "efficient volume sampling for row column subset selection"

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)