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: "approximate supermodularity bounds for experimental design"
  Abstract: This work provides performance guarantees for the greedy solution of experimental design problems. In particular, it focuses on A- and E-optimal designs, for which typical guarantees do not apply since the mean-square error and the maximum eigenvalue of the estimation error covariance matrix are not supermodular. To do so, it leverages the concept of approximate supermodularity to derive non-asymptotic worst-case suboptimality bounds for these greedy solutions. These bounds reveal that as the SNR of the experiments decreases, these cost functions behave increasingly as supermodular functions. As such, greedy A- and E-optimal designs approach (1-1/e)-optimality. These results reconcile the empirical success of greedy experimental design with the non-supermodularity of the A- and E-optimality criteria.

Connected through hyperedge HE-1 (induced by source paper "bayesian experimental design using regularized determinantal point processes"):
  with members:
    - "distributed estimation of the inverse hessian by determinantal averaging"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "submodular maximization beyond non negativity guarantees fast algorithms and applications"):
  with members:
    - "fast maximization of non submodular monotonic functions on the integer lattice"
    - "guess free maximization of submodular and linear sums"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "lazier than lazy greedy"
    - "efficient feature group sequencing for anytime linear prediction"

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)