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: "efficient exact inference in planar ising models"
  Abstract: We give polynomial-time algorithms for the exact computation of lowest-energy (ground) states, worst margin violators, log partition functions, and marginal edge probabilities in certain binary undirected graphical models. Our approach provides an interesting alternative to the well-known graph cut paradigm in that it does not impose any submodularity constraints; instead we require planarity to establish a correspondence with perfect matchings (dimer coverings) in an expanded dual graph. We implement a unified framework while delegating complex but well-understood subproblems (planar embedding, maximum-weight perfect matching) to established algorithms for which efficient implementations are freely available. Unlike graph cut methods, we can perform penalized maximum-likelihood as well as maximum-margin parameter estimation in the associated conditional random fields (CRFs), and employ marginal posterior probabilities as well as maximum a posteriori (MAP) states for prediction. Maximum-margin CRF parameter estimation on image denoising and segmentation problems shows our approach to be efficient and effective. A C++ implementation is available from this http URL

Connected through hyperedge HE-1 (induced by source paper "a new family of tractable ising models"):
  with members:
    - "counting perfect matchings in graphs that exclude a single crossing minor"
    - "a new class of upper bounds on the log partition function"

Connected through hyperedge HE-2 (induced by source paper "tractable minor free generalization of planar zero field ising models"):
  with members:
    - "counting perfect matchings in graphs that exclude a single crossing minor"
    - "a new class of upper bounds on the log partition function"

Connected through hyperedge HE-3 (induced by source paper "large scale log determinant computation through stochastic chebyshev expansions"):
  with members:
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"
    - "improved bounds on sample size for implicit matrix trace estimators"
    - "efficient estimation of eigenvalue counts in an interval"
    - "robust inversion dimensionality reduction and randomized sampling"

Connected through hyperedge HE-4 (induced by source paper "latent sentiment detection in online social networks a communications oriented view"):
  with members:
    - "a class of random fields on complete graphs with tractable partition function"
    - "user level sentiment analysis incorporating social networks"

Connected through hyperedge HE-5 (induced by source paper "essp an efficient approach to minimizing dense and nonsubmodular energy functions"):
  with members:
    - "a submodular supermodular procedure with applications to discriminative structure learning"

Connected through hyperedge HE-6 (induced by source paper "a class of random fields on complete graphs with tractable partition function"):
  with members:
    - "a new class of upper bounds on the log partition function"
    - "norm product belief propagation primal dual message passing for approximate inference"

Connected through hyperedge HE-7 (induced by source paper "fast planar correlation clustering for image segmentation"):
  with members:
    - "tightening lp relaxations for map using message passing"
    - "optimal coalition structures in graph games"
    - "optimal coalition structures in cooperative graph games"

Connected through hyperedge HE-8 (induced by source paper "tightening mrf relaxations with planar subproblems"):
  with members:
    - "tightening lp relaxations for map using message passing"

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)