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: "efficiently searching for frustrated cycles in map inference"
  Abstract: Dual decomposition provides a tractable framework for designing algorithms for finding the most probable (MAP) configuration in graphical models. However, for many real-world inference problems, the typical decomposition has a large integrality gap, due to frustrated cycles. One way to tighten the relaxation is to introduce additional constraints that explicitly enforce cycle consistency. Earlier work showed that cluster-pursuit algorithms, which iteratively introduce cycle and other higherorder consistency constraints, allows one to exactly solve many hard inference problems. However, these algorithms explicitly enumerate a candidate set of clusters, limiting them to triplets or other short cycles. We solve the search problem for cycle constraints, giving a nearly linear time algorithm for finding the most frustrated cycle of arbitrary length. We show how to use this search algorithm together with the dual decomposition framework and clusterpursuit. The new algorithm exactly solves MAP inference problems arising from relational classification and stereo vision.

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 "exact inference in structured prediction"):
  with members:
    - "learning latent variable structured prediction models with gaussian perturbations"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "exact recovery in the stochastic block model"
    - "search based structured prediction"

Connected through hyperedge HE-3 (induced by source paper "map inference via l2 sphere linear program reformulation"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "bethe admm for tree decomposition based parallel map inference"
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-4 (induced by source paper "accelerated inference in markov random fields via smooth riemannian optimization"):
  with members:
    - "bonnet an open source training and deployment framework for semantic segmentation in robotics using cnns"
    - "large scale binary quadratic optimization using semidefinite relaxation and applications"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a survey of semantic segmentation"
    - "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation"
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"

Connected through hyperedge HE-5 (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-6 (induced by source paper "continuous relaxation of map inference a nonconvex perspective"):
  with members:
    - "alternating direction graph matching"
    - "a new look at reweighted message passing"
    - "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"

Connected through hyperedge HE-7 (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"
    - "tightening lp relaxations for map using message passing"
    - "continuous multiclass labeling approaches and algorithms"
    - "generalized roof duality and bisubmodular functions"

Connected through hyperedge HE-8 (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"
    - "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.DS(Data Structures and Algorithms)