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: "exact sampling of determinantal point processes with sublinear time preprocessing"
  Abstract: We study the complexity of sampling from a distribution over all index subsets of the set $\{1,...,n\}$ with the probability of a subset $S$ proportional to the determinant of the submatrix $\mathbf{L}_S$ of some $n\times n$ p.s.d. matrix $\mathbf{L}$, where $\mathbf{L}_S$ corresponds to the entries of $\mathbf{L}$ indexed by $S$. Known as a determinantal point process, this distribution is used in machine learning to induce diversity in subset selection. In practice, we often wish to sample multiple subsets $S$ with small expected size $k = E[|S|] \ll n$ from a very large matrix $\mathbf{L}$, so it is important to minimize the preprocessing cost of the procedure (performed once) as well as the sampling cost (performed repeatedly). For this purpose, we propose a new algorithm which, given access to $\mathbf{L}$, samples exactly from a determinantal point process while satisfying the following two properties: (1) its preprocessing cost is $n \cdot \text{poly}(k)$, i.e., sublinear in the size of $\mathbf{L}$, and (2) its sampling cost is $\text{poly}(k)$, i.e., independent of the size of $\mathbf{L}$. Prior to our results, state-of-the-art exact samplers required $O(n^3)$ preprocessing time and sampling time linear in $n$ or dependent on the spectral properties of $\mathbf{L}$. We also give a reduction which allows using our algorithm for exact sampling from cardinality constrained determinantal point processes with $n\cdot\text{poly}(k)$ time preprocessing.

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"
    - "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"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "greedy column subset selection new bounds and distributed algorithms"

Connected through hyperedge HE-2 (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"
    - "fairness risk measures"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "minimizing the maximal loss how and why"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "dppy sampling dpps with python"):
  with members:
    - "learning nonsymmetric determinantal point processes"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "batched gaussian process bandit optimization via determinantal point processes"

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)