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: "tightening lp relaxations for map using message passing"
  Abstract: Linear Programming (LP) relaxations have become powerful tools for finding the most probable (MAP) configuration in graphical models. These relaxations can be solved efficiently using message-passing algorithms such as belief propagation and, when the relaxation is tight, provably find the MAP configuration. The standard LP relaxation is not tight enough in many real-world problems, however, and this has lead to the use of higher order cluster-based LP relaxations. The computational cost increases exponentially with the size of the clusters and limits the number and type of clusters we can use. We propose to solve the cluster selection problem monotonically in the dual LP, iteratively selecting clusters with guaranteed improvement, and quickly re-solving with the added clusters by reusing the existing solution. Our dual message-passing algorithm finds the MAP configuration in protein sidechain placement, protein design, and stereo problems, in cases where the standard LP relaxation fails.

Connected through hyperedge HE-1 (induced by source paper "graph structured prediction energy networks"):
  with members:
    - "deep structured prediction with nonlinear output transformations"
    - "learning approximate inference networks for structured prediction"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "structured prediction energy networks"
    - "deeply learning the messages in message passing inference"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "worst case polynomial time exact map inference on discrete models with global dependencies"):
  with members:
    - "optimizing for measure of performance in max margin parsing"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "a compact linear programming relaxation for binary sub modular mrf"
    - "tighter linear program relaxations for high order graphical models"
    - "discriminative probabilistic models for relational data"
    - "fast exact inference for recursive cardinality models"
    - "a tutorial on dual decomposition and lagrangian relaxation for inference in natural language processing"
    - "complexity of inference in graphical models"

Connected through hyperedge HE-3 (induced by source paper "deep structured prediction with nonlinear output transformations"):
  with members:
    - "learning approximate inference networks for structured prediction"
    - "searnn training rnns with global local losses"
    - "input convex neural networks"
    - "pixel recurrent neural networks"
    - "structured prediction energy networks"
    - "deeply learning the messages in message passing inference"
    - "deep residual learning for image recognition"
    - "fully connected deep structured networks"

Connected through hyperedge HE-4 (induced by source paper "graphical models and belief propagation hierarchy for physics constrained network flows"):
  with members:
    - "how to calculate partition functions using convex programming hierarchies provable bounds for variational methods"
    - "minimum weight perfect matching via blossom belief propagation"
    - "a differential analysis of the power flow equations"
    - "solving the power flow equations a monotone operator approach"
    - "submodular functions from discrete to continous domains"
    - "natural gas flow solutions with guarantees a monotone operator theory approach"
    - "optimal compression in natural gas networks a geometric programming approach"
    - "the power of linear programming for general valued csps"

Connected through hyperedge HE-5 (induced by source paper "a discriminative view of mrf pre processing algorithms"):
  with members:
    - "full flow optical flow estimation by global optimization over regular grids"
    - "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"
    - "efficiently searching for frustrated cycles in map inference"
    - "continuous multiclass labeling approaches and algorithms"
    - "generalized roof duality and bisubmodular functions"

Connected through hyperedge HE-6 (induced by source paper "large scale binary quadratic optimization using semidefinite relaxation and applications"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "scalable semidefinite relaxation for maximum a posterior estimation"
    - "a fast semidefinite approach to solving binary quadratic problems"
    - "a hybrid algorithm for convex semidefinite optimization"
    - "opengm a c library for discrete graphical models"
    - "efficiently searching for frustrated cycles in map inference"

Connected through hyperedge HE-7 (induced by source paper "newton type methods for inference in higher order markov random fields"):
  with members:
    - "a new look at reweighted message passing"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "convergent message passing algorithms a unifying view"
    - "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-8 (induced by source paper "partial optimality of dual decomposition for map inference in pairwise mrfs"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "a compact linear programming relaxation for binary sub modular mrf"
    - "a tutorial on dual decomposition and lagrangian relaxation for inference in natural language processing"
    - "on the optimality of tree reweighted max product 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.DS(Data Structures and Algorithms)