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: "complexity of inference in graphical models"
  Abstract: It is well-known that inference in graphical models is hard in the worst case, but tractable for models with bounded treewidth. We ask whether treewidth is the only structural criterion of the underlying graph that enables tractable inference. In other words, is there some class of structures with unbounded treewidth in which inference is tractable? Subject to a combinatorial hypothesis due to Robertson et al. (1994), we show that low treewidth is indeed the only structural restriction that can ensure tractability. Thus, even for the "best case" graph structure, there is no inference algorithm with complexity polynomial in the treewidth.

Connected through hyperedge HE-1 (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"
    - "tightening lp relaxations for map using message passing"

Connected through hyperedge HE-2 (induced by source paper "resolving language and vision ambiguities together joint segmentation prepositional attachment resolution in captioned scenes"):
  with members:
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "do you see what i mean visual resolution of linguistic ambiguities"
    - "vqa visual question answering"
    - "visual madlibs fill in the blank image generation and question answering"
    - "ask your neurons a neural based approach to answering questions about images"
    - "cider consensus based image description evaluation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"

Connected through hyperedge HE-3 (induced by source paper "the sum product theorem a foundation for learning tractable models"):
  with members:
    - "recursive decomposition for nonconvex optimization"
    - "a differential approach to inference in bayesian networks"
    - "value elimination bayesian inference via backtracking search"
    - "algebraic model counting"
    - "sum product networks a new deep architecture"
    - "the relationship between and or search and variable elimination"
    - "solving sat and bayesian inference with backtracking search"
    - "the language of search"

Connected through hyperedge HE-4 (induced by source paper "tractable lineages on treelike instances limits and extensions"):
  with members:
    - "provenance circuits for trees and treelike instances extended version"
    - "approximate lifted inference with probabilistic databases"
    - "polynomial bounds for the grid minor theorem"
    - "lower bounds for the complexity of monadic second order logic"
    - "are there any good digraph width measures"
    - "algorithmic meta theorems"
    - "tractable hypergraph properties for constraint satisfaction and conjunctive queries"
    - "the dichotomy of conjunctive queries on probabilistic structures"

Connected through hyperedge HE-5 (induced by source paper "message passing and combinatorial optimization"):
  with members:
    - "augmentative message passing for traveling salesman problem and graph partitioning"
    - "perturbed message passing for constraint satisfaction problems"
    - "revisiting algebra and complexity of inference in graphical models"
    - "on map inference by mwss on perfect graphs"
    - "loopy belief propagation for approximate inference an empirical study"
    - "graphical models for game theory"
    - "message passing algorithms reparameterizations and splittings"
    - "the bethe permanent of a nonnegative matrix"

Connected through hyperedge HE-6 (induced by source paper "minimum weight perfect matching via blossom belief propagation"):
  with members:
    - "max product belief propagation for linear programming applications to combinatorial optimization"
    - "the cutting plane method is polynomial for perfect matchings"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "belief propagation for weighted b matchings on arbitrary graphs and its relation to linear programs with integer solutions"
    - "belief propagation for min cost network flow convergence and correctness"
    - "message passing for maximum weight independent set"
    - "max product for maximum weight matching convergence correctness and lp duality"

Connected through hyperedge HE-7 (induced by source paper "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"):
  with members:
    - "incremental knowledge base construction using deepdive"
    - "dimmwitted a study of main memory statistical analytics"
    - "projecting markov random field parameters for fast mixing"
    - "tractable hypergraph properties for constraint satisfaction and conjunctive queries"

Connected through hyperedge HE-8 (induced by source paper "advances in learning bayesian networks of bounded treewidth"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "the bayesian structural em algorithm"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "advances in bayesian network learning using integer programming"
    - "bayesian network learning with cutting planes"
    - "exact structure discovery in bayesian networks with less space"

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)