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: "efficient sampling for k determinantal point processes"
  Abstract: Determinantal Point Processes (DPPs) are elegant probabilistic models of repulsion and diversity over discrete sets of items. But their applicability to large sets is hindered by expensive cubic-complexity matrix operations for basic tasks such as sampling. In light of this, we propose a new method for approximate sampling from discrete $k$-DPPs. Our method takes advantage of the diversity property of subsets sampled from a DPP, and proceeds in two stages: first it constructs coresets for the ground set of items; thereafter, it efficiently samples subsets based on the constructed coresets. As opposed to previous approaches, our algorithm aims to minimize the total variation distance to the original distribution. Experiments on both synthetic and real datasets indicate that our sampling algorithm works efficiently on large data sets, and yields more accurate samples than previous approaches.

Connected through hyperedge HE-1 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "verdictdb universalizing approximate query processing"
    - "measuring the effects of data parallelism on neural network training"
    - "hemingway modeling distributed optimization algorithms"
    - "unbiased estimates for linear regression via volume sampling"
    - "database learning toward a database that becomes smarter every time"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-2 (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-3 (induced by source paper "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"):
  with members:
    - "correcting the bias in least squares regression with volume rescaled sampling"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "lectures on randomized numerical linear algebra"
    - "unbiased estimates for linear regression via volume sampling"
    - "subsampling for ridge regression via regularized volume sampling"

Connected through hyperedge HE-4 (induced by source paper "structured monte carlo sampling for nonisotropic distributions via determinantal point processes"):
  with members:
    - "when random search is not enough sample efficient and noise robust blackbox optimization of rl policies"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"
    - "orthogonal random features"
    - "benchmarking deep reinforcement learning for continuous control"
    - "adam a method for stochastic optimization"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-5 (induced by source paper "nystrom landmark sampling and regularized christoffel functions"):
  with members:
    - "approximation beats concentration an approximation view on inference with smooth radial kernels"
    - "relating leverage scores and density using regularized christoffel functions"
    - "online row sampling"
    - "column selection via adaptive sampling"
    - "provable deterministic leverage score sampling"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "sharp analysis of low rank kernel matrix approximations"

Connected through hyperedge HE-6 (induced by source paper "fast determinantal point processes via distortion free intermediate sampling"):
  with members:
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "optimal cur matrix decompositions"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "fast approximation of matrix coherence and statistical leverage"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-7 (induced by source paper "fair and diverse dpp based data summarization"):
  with members:
    - "semantics derived automatically from language corpora contain human like biases"
    - "measuring fairness in ranked outputs"
    - "on the complexity of constrained determinantal point processes"
    - "learning mixtures of submodular shells with application to document summarization"
    - "fairness through awareness"

Connected through hyperedge HE-8 (induced by source paper "a polynomial time mcmc method for sampling from continuous dpps"):
  with members:
    - "exact sampling from determinantal point processes"
    - "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)