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: "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
  Abstract: Gibbs sampling is a Markov chain Monte Carlo technique commonly used for estimating marginal distributions. To speed up Gibbs sampling, there has recently been interest in parallelizing it by executing asynchronously. While empirical results suggest that many models can be efficiently sampled asynchronously, traditional Markov chain analysis does not apply to the asynchronous case, and thus asynchronous Gibbs sampling is poorly understood. In this paper, we derive a better understanding of the two main challenges of asynchronous Gibbs: bias and mixing time. We show experimentally that our theoretical results match practical outcomes.

Connected through hyperedge HE-1 (induced by source paper "distributed metropolis sampler with optimal parallelism"):
  with members:
    - "dynamic sampling from graphical models"
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "hogwild gibbs can be panaccurate"
    - "what can be sampled locally"
    - "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-2 (induced by source paper "fully asynchronous distributed metropolis sampler with optimal speedup"):
  with members:
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "what can be sampled locally"
    - "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-3 (induced by source paper "regression from dependent observations"):
  with members:
    - "hogwild gibbs can be panaccurate"
    - "learning graphical models using multiplicative weights"
    - "information theoretic properties of markov random fields and their algorithmic applications"
    - "testing ising models"
    - "social contagion theory examining dynamic social networks and human behavior"

Connected through hyperedge HE-4 (induced by source paper "what can be sampled locally"):
  with members:
    - "on local distributed sampling and counting"
    - "distributed degree splitting edge coloring and orientations"
    - "bis hardness for 2 spin systems on bipartite bounded degree graphs in the tree non uniqueness region"
    - "uniform sampling through the lov asz local lemma"
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"
    - "an exponential separation between randomized and deterministic complexity in the local model"
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"
    - "on the complexity of local distributed graph problems"

Connected through hyperedge HE-5 (induced by source paper "streaming graph challenge stochastic block partition"):
  with members:
    - "static graph challenge subgraph isomorphism"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"
    - "a high performance implementation of spectral clustering on cpu gpu platforms"
    - "an incremental local first community detection method for dynamic graphs"
    - "fast detection of overlapping communities via online tensor methods"
    - "bayesian discovery of threat networks"
    - "network sampling from static to streaming graphs"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-6 (induced by source paper "deepbach a steerable model for bach chorales generation"):
  with members:
    - "style imitation and chord invention in polyphonic music with exponential families"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning longer memory in recurrent neural networks"
    - "constructing long short term memory based deep recurrent neural networks for large vocabulary speech recognition"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

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)