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: "opengm a c library for discrete graphical models"
  Abstract: OpenGM is a C++ template library for defining discrete graphical models and performing inference on these models, using a wide range of state-of-the-art algorithms. No restrictions are imposed on the factor graph to allow for higher-order factors and arbitrary neighborhood structures. Large models with repetitive structure are handled efficiently because (i) functions that occur repeatedly need to be stored only once, and (ii) distinct functions can be implemented differently, using different encodings alongside each other in the same model. Several parametric functions (e.g. metrics), sparse and dense value tables are provided and so is an interface for custom C++ code. Algorithms are separated by design from the representation of graphical models and are easily exchangeable. OpenGM, its algorithms, HDF5 file format and command line tools are modular and extendible.

Connected through hyperedge HE-1 (induced by source paper "referring expression grounding by marginalizing scene graph likelihood"):
  with members:
    - "learning to compose and reason with language tree structures for visual grounding"
    - "using syntax to ground referring expressions in natural images"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "relational inductive biases deep learning and graph networks"
    - "visual question reasoning on general dependency tree"

Connected through hyperedge HE-2 (induced by source paper "learning to find common objects across few image collections"):
  with members:
    - "attention based deep multiple instance learning"
    - "prototypical networks for few shot learning"
    - "deep self taught learning for weakly supervised object localization"
    - "revisiting knowledge transfer for training object class detectors"
    - "few shot image recognition by predicting parameters from activations"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "rapid adaptation with conditionally shifted neurons"
    - "weakly supervised object localization using things and stuff transfer"

Connected through hyperedge HE-3 (induced by source paper "revisiting visual grounding"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "differentiable scene graphs"
    - "referring relationships"
    - "using syntax to ground referring expressions in natural images"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "visual referring expression recognition what do systems actually learn"
    - "open vocabulary phrase detection"
    - "grounding referring expressions in images by variational context"

Connected through hyperedge HE-4 (induced by source paper "on preempting advanced persistent threats using probabilistic graphical models"):
  with members:
    - "vulnerable open source dependencies counting those that matter"
    - "on the inability of markov models to capture criticality in human mobility"
    - "snorkel rapid training data creation with weak supervision"
    - "catching worms trojan horses and pups unsupervised detection of silent delivery campaigns"
    - "thou shalt not depend on me analysing the use of outdated javascript libraries on the web"
    - "attack analysis results for adversarial engagement 1 of the darpa transparent computing program"
    - "towards detecting compromised accounts on social networks"

Connected through hyperedge HE-5 (induced by source paper "depth from a polarisation rgb stereo pair"):
  with members:
    - "linear differential constraints for photo polarimetric height estimation"

Connected through hyperedge HE-6 (induced by source paper "a data driven prior on facet orientation for semantic mesh labeling"):
  with members:
    - "multi view stereo with single view semantic mesh refinement"
    - "semantically informed multiview surface refinement"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "semantic 3d reconstruction with continuous regularization and ray potentials using a visibility consistency constraint"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "efficient 2d and 3d facade segmentation using auto context"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "incremental reconstruction of urban environments by edge points delaunay triangulation"

Connected through hyperedge HE-7 (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"
    - "efficiently searching for frustrated cycles in map inference"

Connected through hyperedge HE-8 (induced by source paper "online mutual foreground segmentation for multispectral stereo videos"):
  with members:
    - "segflow joint learning for video object segmentation and optical flow"
    - "fast optical flow using dense inverse search"
    - "one shot video object segmentation"
    - "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation"
    - "non negative matrix factorization with sparseness constraints"

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)