human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "approximate inference via weighted rademacher complexity"):
  Abstract: Rademacher complexity is often used to characterize the learnability of a hypothesis class and is known to be related to the class size. We leverage this observation and introduce a new technique for estimating the size of an arbitrary weighted set, defined as the sum of weights of all elements in the set. Our technique provides upper and lower bounds on a novel generalization of Rademacher complexity to the weighted setting in terms of the weighted set size. This generalizes Massart's Lemma, a known upper bound on the Rademacher complexity in terms of the unweighted set size. We show that the weighted Rademacher complexity can be estimated by solving a randomly perturbed optimization problem, allowing us to derive high-probability bounds on the size of any weighted set. We apply our method to the problems of calculating the partition function of an Ising model and computing propositional model counts (#SAT). Our experiments demonstrate that we can produce tighter bounds than competing methods in both the weighted and unweighted settings.

Includes members:
  Member "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sampled softmax with random fourier features")
  Member "high dimensional inference with random maximum a posteriori perturbations":
    also connected through other hyperedges:
      (none)
  Member "provable submodular minimization using wolfe s algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quadratic decomposable submodular function minimization theory and practice")
  Member "a scalable approximate model counter":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantitative verification of neural networks and its security applications")
  Member "taming the curse of dimensionality discrete integration by hashing and optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards efficient discrete integration via adaptive quantile queries")
  Member "optimization with parity constraints from binary codes to discrete integration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quantitative verification of neural networks and its security applications")
  Member "on sampling from the gibbs distribution with random maximum a posteriori perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning with differentiable perturbed optimizers")
  Member "on the partition function and random maximum a posteriori perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning with differentiable perturbed optimizers")

Question: Please tell me which class the source paper 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)