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: "learning fast mixing models for structured prediction"
  Abstract: Markov Chain Monte Carlo (MCMC) algorithms are often used for approximate inference inside learning, but their slow mixing can be difficult to diagnose and the approximations can seriously degrade learning. To alleviate these issues, we define a new model family using strong Doeblin Markov chains, whose mixing times can be precisely controlled by a parameter. We also develop an algorithm to learn such models, which involves maximizing the data likelihood under the induced stationary distribution of these chains. We show empirical improvements on two challenging inference tasks.

Connected through hyperedge HE-1 (induced by source paper "efficient algorithms for personalized pagerank"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "a note on the pagerank of undirected graphs"
    - "fast bidirectional probability estimation in markov models"
    - "fast distributed pagerank computation"
    - "bidirectional pagerank estimation from average case to worst case"
    - "heat kernel based community detection"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "computing the stationary distribution locally"

Connected through hyperedge HE-2 (induced by source paper "reified context models"):
  with members:
    - "mean field networks"
    - "exchangeable variable models"
    - "sum product networks a new deep architecture"
    - "a generalized mean field algorithm for variational inference in exponential families"
    - "tightening lp relaxations for map using message passing"
    - "a new class of upper bounds on the log partition function"
    - "learning dependency based compositional semantics"
    - "search based structured prediction"

Connected through hyperedge HE-3 (induced by source paper "fast bidirectional probability estimation in markov models"):
  with members:
    - "heat kernel based community detection"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "computing the stationary distribution locally"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-4 (induced by source paper "maximum likelihood learning with arbitrary treewidth via fast mixing parameter sets"):
  with members:
    - "projecting ising model parameters for fast mixing"
    - "projecting markov random field parameters for fast mixing"
    - "asymptotic efficiency of deterministic estimators for discrete energy based models ratio matching and pseudolikelihood"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

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)