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: "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
  Abstract: Strongly Rayleigh distributions are natural generalizations of product and determinantal probability distributions and satisfy strongest form of negative dependence properties. We show that the "natural" Monte Carlo Markov Chain (MCMC) is rapidly mixing in the support of a {\em homogeneous} strongly Rayleigh distribution. As a byproduct, our proof implies Markov chains can be used to efficiently generate approximate samples of a $k$-determinantal point process. This answers an open question raised by Deshpande and Rademacher.

Connected through hyperedge HE-1 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method"):
  with members:
    - "two models of double descent for weak features"
    - "exact sampling of determinantal point processes with sublinear time preprocessing"
    - "bayesian experimental design using regularized determinantal point processes"
    - "exact expressions for double descent and implicit regularization via surrogate random design"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "reverse iterative volume sampling for linear regression"
    - "greedy column subset selection new bounds and distributed algorithms"

Connected through hyperedge HE-2 (induced by source paper "diversity and inclusion metrics for subset selection"):
  with members:
    - "assessing and remedying coverage for a given dataset"
    - "the price of fair pca one extra dimension"
    - "ranking with fairness constraints"
    - "measuring fairness in ranked outputs"
    - "how to be fair and diverse"
    - "learning mixtures of submodular shells with application to document summarization"
    - "fairness through awareness"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-3 (induced by source paper "batch active learning using determinantal point processes"):
  with members:
    - "diverse mini batch active learning"
    - "uncertainty sampling is preconditioned stochastic gradient descent on zero one loss"
    - "active model learning and diverse action sampling for task and motion planning"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "batch active preference based learning of reward functions"
    - "determinantal point processes for mini batch diversification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning preferences for manipulation tasks from online coactive feedback"

Connected through hyperedge HE-4 (induced by source paper "adaptive sampling for stochastic risk averse learning"):
  with members:
    - "multi player bandits the adversarial case"
    - "distributionally robust optimization and generalization in kernel methods"
    - "introduction to online convex optimization"
    - "exact sampling of determinantal point processes with sublinear time preprocessing"
    - "fairness risk measures"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "minimizing the maximal loss how and why"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "deep batch active learning by diverse uncertain gradient lower bounds"):
  with members:
    - "on the difficulty of warm starting neural network training"
    - "adversarial active learning for deep networks a margin based approach"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "reverse iterative volume sampling for linear regression"
    - "libact pool based active learning in python"
    - "determinantal point processes for mini batch diversification"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"

Connected through hyperedge HE-6 (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"
    - "kronecker determinantal point processes"
    - "diversity networks neural network compression using determinantal point processes"

Connected through hyperedge HE-7 (induced by source paper "learning nonsymmetric determinantal point processes"):
  with members:
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "kronecker determinantal point processes"
    - "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-8 (induced by source paper "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"):
  with members:
    - "approximately counting bases of bicircular matroids"
    - "log concave polynomials i entropy and a deterministic approximation algorithm for counting bases of matroids"
    - "high dimensional random walks and colorful expansion"
    - "random cluster dynamics for the ising model is rapidly mixing"
    - "approximating the tutte polynomial of a binary matroid and other related combinatorial polynomials"
    - "the complexity of computing the sign of the tutte polynomial"
    - "inapproximability of the tutte polynomial of a planar graph"
    - "approximating the partition function of the ferromagnetic potts model"

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)