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: "kronecker determinantal point processes"
  Abstract: Determinantal Point Processes (DPPs) are probabilistic models over all subsets a ground set of $N$ items. They have recently gained prominence in several applications that rely on "diverse" subsets. However, their applicability to large problems is still limited due to the $\mathcal O(N^3)$ complexity of core tasks such as sampling and learning. We enable efficient sampling and learning for DPPs by introducing KronDPP, a DPP model whose kernel matrix decomposes as a tensor product of multiple smaller kernel matrices. This decomposition immediately enables fast exact sampling. But contrary to what one may expect, leveraging the Kronecker product structure for speeding up DPP learning turns out to be more difficult. We overcome this challenge, and derive batch and stochastic optimization algorithms for efficiently learning the parameters of a KronDPP.

Connected through hyperedge HE-1 (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"
    - "diversity networks neural network compression using determinantal point processes"

Connected through hyperedge HE-2 (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"
    - "fixed point algorithms for learning determinantal point processes"
    - "adam a method for stochastic optimization"
    - "learning mixtures of submodular shells with application to document summarization"

Connected through hyperedge HE-3 (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"
    - "fixed point algorithms for learning 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-4 (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"
    - "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"
    - "notes on using determinantal point processes for clustering with applications to text clustering"

Connected through hyperedge HE-5 (induced by source paper "boltzmann machines and energy based models"):
  with members:
    - "boltzmann machines for time series"
    - "calibrating energy based generative adversarial networks"
    - "reinforcement learning with deep energy based policies"
    - "energy based generative adversarial network"
    - "deep structured energy based models for anomaly detection"
    - "deep directed generative models with energy based probability estimation"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"

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)