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 "taming the curse of dimensionality discrete integration by hashing and optimization"):
  Abstract: Integration is affected by the curse of dimensionality and quickly becomes intractable as the dimensionality of the problem grows. We propose a randomized algorithm that, with high probability, gives a constant-factor approximation of a general discrete integral defined over an exponentially large set. This algorithm relies on solving only a small number of instances of a discrete combinatorial optimization problem subject to randomly generated parity constraints used as a hash function. As an application, we demonstrate that with a small number of MAP queries we can efficiently approximate the partition function of discrete graphical models, which can in turn be used, for instance, for marginal computation or model selection.

Includes members:
  Member "loopy belief propagation for approximate inference an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "a scalable and nearly uniform generator of sat witnesses":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constrained counting and sampling bridging the gap between theory and practice")
  Member "improving the accuracy and efficiency of map inference for markov logic":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond markov logic efficient mining of prediction rules in large graphs")
  Member "bayesian learning in undirected graphical models approximate mcmc algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a theoretical connection between statistical physics and reinforcement learning")
  Member "map complexity results and approximation methods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a rate distortion framework for explaining neural network decisions")
  Member "on the partition function and random maximum a posteriori perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning with differentiable perturbed optimizers")
  Member "tightening lp relaxations for map using message passing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph structured prediction energy networks")
  Member "a decidable dichotomy theorem on directed graph homomorphisms with non negative weights":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a dichotomy for bounded degree graph homomorphisms with nonnegative weights")

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)