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: "a new look at reweighted message passing"
  Abstract: We propose a new family of message passing techniques for MAP estimation in graphical models which we call   Sequential Reweighted Message Passing  (SRMP). Special cases include well-known techniques such as   Min-Sum Diffusion  (MSD) and a faster  Sequential Tree-Reweighted Message Passing  (TRW-S). Importantly, our derivation is simpler than the original derivation of TRW-S, and does not involve a decomposition into trees. This allows easy generalizations. The new family of algorithms can be viewed as a generalization of TRW-S from pairwise to higher-order graphical models. We test SRMP on several real-world problems with promising results.

Connected through hyperedge HE-1 (induced by source paper "map inference via block coordinate frank wolfe algorithm"):
  with members:
    - "efficient linear programming for dense crfs"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "a dual ascent framework for lagrangean decomposition of combinatorial problems"
    - "submodular relaxation for inference in markov random fields"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "a multi plane block coordinate frank wolfe algorithm for training structural svms with a costly max oracle"
    - "block coordinate frank wolfe optimization for structural svms"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-2 (induced by source paper "continuous relaxation of map inference a nonconvex perspective"):
  with members:
    - "alternating direction graph matching"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "opengm a c library for discrete graphical models"
    - "efficiently searching for frustrated cycles in map inference"

Connected through hyperedge HE-3 (induced by source paper "improving ics cyber resilience through optimal diversification of network resources"):
  with members:
    - "the multiple facets of software diversity recent developments in year 2000 and beyond"

Connected through hyperedge HE-4 (induced by source paper "discrete continuous admm for transductive inference in higher order mrfs"):
  with members:
    - "analysis and optimization of loss functions for multiclass top k and multilabel classification"
    - "a message passing algorithm for the minimum cost multicut problem"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "secrets of grabcut and kernel k means"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "a convex relaxation for weakly supervised classifiers"

Connected through hyperedge HE-5 (induced by source paper "newton type methods for inference in higher order markov random fields"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "convergent message passing algorithms a unifying view"
    - "tightening lp relaxations for map using message passing"
    - "an embarrassingly simple speed up of belief propagation with robust potentials"
    - "norm product belief propagation primal dual message passing for approximate inference"

Connected through hyperedge HE-6 (induced by source paper "a message passing algorithm for the minimum cost multicut problem"):
  with members:
    - "a dual ascent framework for lagrangean decomposition of combinatorial problems"
    - "moral lineage tracing"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "a multiscale framework for challenging discrete optimization"
    - "efficiently searching for frustrated cycles in map inference"

Connected through hyperedge HE-7 (induced by source paper "a dual ascent framework for lagrangean decomposition of combinatorial problems"):
  with members:
    - "a message passing algorithm for the minimum cost multicut problem"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "fast planar correlation clustering for image segmentation"
    - "convergent message passing algorithms a unifying view"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-8 (induced by source paper "higher order maximum persistency and comparison theorems"):
  with members:
    - "maximum persistency via iterative relaxed inference with graphical models"
    - "maximum persistency in energy minimization"
    - "partial optimality by pruning for map inference with general graphical models"
    - "potts model parametric maxflow and k submodular functions"
    - "generalized sequential tree reweighted message passing"
    - "the power of linear programming for valued csps"
    - "efficiently searching for frustrated cycles in map inference"
    - "the power of linear programming for valued csps a constructive characterization"

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.AI(Artificial Intelligence)