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: "fixed point algorithms for learning determinantal point processes"
  Abstract: Determinantal point processes (DPPs) offer an elegant tool for encoding probabilities over subsets of a ground set. Discrete DPPs are parametrized by a positive semidefinite matrix (called the DPP kernel), and estimating this kernel is key to learning DPPs from observed data. We consider the task of learning the DPP kernel, and develop for it a surprisingly simple yet effective new algorithm. Our algorithm offers the following benefits over previous approaches: (a) it is much simpler; (b) it yields equally good and sometimes even better local maxima; and (c) it runs an order of magnitude faster on large problems. We present experimental results on both real and simulated data to illustrate the numerical performance of our technique.

Connected through hyperedge HE-1 (induced by source paper "diversity in machine learning"):
  with members:
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "tagging like humans diverse and distinct image annotation"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "diversity promoting bayesian learning of latent variable models"
    - "learning detection with diverse proposals"
    - "diversified texture synthesis with feed forward networks"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "an ensemble diversity approach to supervised binary hashing"

Connected through hyperedge HE-2 (induced by source paper "flexible modeling of diversity with strongly log concave distributions"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "fair and diverse dpp based data summarization"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "gdpp learning diverse generations using determinantal point process"
    - "guess free maximization of submodular and linear sums"
    - "unbiased estimates for linear regression via volume sampling"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "kronecker determinantal point processes"

Connected through hyperedge HE-3 (induced by source paper "learning nonsymmetric determinantal point processes"):
  with members:
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "kronecker determinantal point processes"
    - "adam a method for stochastic optimization"
    - "learning mixtures of submodular shells with application to document summarization"

Connected through hyperedge HE-4 (induced by source paper "recent advances in diversified recommendation"):
  with members:
    - "explainable recommendation a survey and new perspectives"
    - "adaptive personalized diversity for visual discovery"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-5 (induced by source paper "learning determinantal point processes by corrective negative sampling"):
  with members:
    - "adversarial contrastive estimation"
    - "improving negative sampling for word representation using self embedded features"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "training region based object detectors with online hard example mining"
    - "kronecker determinantal point processes"
    - "distributed representations of words and phrases and their compositionality"
    - "a fast and simple algorithm for training neural probabilistic language models"

Connected through hyperedge HE-6 (induced by source paper "learning determinantal point processes by sampling inferred negatives"):
  with members:
    - "improving negative sampling for word representation using self embedded features"
    - "training region based object detectors with online hard example mining"
    - "distributed representations of words and phrases and their compositionality"
    - "a fast and simple algorithm for training neural probabilistic language models"
    - "learning mixtures of submodular shells with application to document summarization"

Connected through hyperedge HE-7 (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"
    - "predicting important objects for egocentric video summarization"
    - "microsoft coco captions data collection and evaluation server"
    - "notes on using determinantal point processes for clustering with applications to text clustering"

Connected through hyperedge HE-8 (induced by source paper "kronecker determinantal point processes"):
  with members:
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "efficient sampling for k determinantal point processes"
    - "diversifying sparsity using variational determinantal point processes"
    - "learning mixtures of submodular shells with application to document summarization"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

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)