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: "fast exact inference for recursive cardinality models"
  Abstract: Cardinality potentials are a generally useful class of high order potential that affect probabilities based on how many of D binary variables are active. Maximum a posteriori (MAP) inference for cardinality potential models is well-understood, with efficient computations taking O(DlogD) time. Yet efficient marginalization and sampling have not been addressed as thoroughly in the machine learning community. We show that there exists a simple algorithm for computing marginal probabilities and drawing exact joint samples that runs in O(Dlog2 D) time, and we show how to frame the algorithm as efficient belief propagation in a low order tree-structured model that includes additional auxiliary variables. We then develop a new, more general class of models, termed Recursive Cardinality models, which take advantage of this efficiency. Finally, we show how to do efficient exact inference in models composed of a tree structure and a cardinality potential. We explore the expressive power of Recursive Cardinality models and empirically demonstrate their utility.

Connected through hyperedge HE-1 (induced by source paper "the limited multi label projection layer"):
  with members:
    - "linknet relational embedding for scene graph"
    - "smooth loss functions for deep top k classification"
    - "predict and constrain modeling cardinality in deep structured prediction"
    - "sparse and constrained attention for neural machine translation"
    - "neural motifs scene graph parsing with global context"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "on support relations and semantic scene graphs"

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"
    - "a tutorial on dual decomposition and lagrangian relaxation for inference in natural language processing"
    - "tightening lp relaxations for map using message passing"
    - "complexity of inference in graphical models"

Connected through hyperedge HE-3 (induced by source paper "predict and constrain modeling cardinality in deep structured prediction"):
  with members:
    - "learning to learn by gradient descent by gradient descent"
    - "unrolled generative adversarial networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "highway and residual networks learn unrolled iterative estimation"
    - "structured prediction energy networks"
    - "visual recognition by counting instances a multi instance cardinality potential kernel"
    - "fully connected deep structured networks"
    - "learning deep structured models"

Connected through hyperedge HE-4 (induced by source paper "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"):
  with members:
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "recurrent convolutional neural network regression for continuous pain intensity estimation in video"
    - "multi instance dynamic ordinal random fields for weakly supervised pain intensity estimation"
    - "variable state latent conditional random fields for facial expression recognition and action unit detection"
    - "multiple instance learning by discriminative training of markov networks"

Connected through hyperedge HE-5 (induced by source paper "hierarchical scene parsing by weakly supervised learning with image descriptions"):
  with members:
    - "deep structured scene parsing by learning with image descriptions"
    - "semantic image segmentation via deep parsing network"
    - "deep hierarchical parsing for semantic segmentation"
    - "holistically nested edge detection"
    - "instance aware semantic segmentation via multi task network cascades"
    - "conditional random fields as recurrent neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "constrained convolutional neural networks for weakly supervised segmentation"

Connected through hyperedge HE-6 (induced by source paper "learning deep structured network for weakly supervised change detection"):
  with members:
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "learning to compare image patches via convolutional neural networks"
    - "constrained convolutional neural networks for weakly supervised segmentation"
    - "what s the point semantic segmentation with point supervision"
    - "cost sensitive learning of deep feature representations from imbalanced data"
    - "very deep convolutional networks for large scale image recognition"
    - "learning rich features from rgb d images for object detection and segmentation"
    - "from image level to pixel level labeling with convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "deep structured scene parsing by learning with image descriptions"):
  with members:
    - "semantic image segmentation via deep parsing network"
    - "deep hierarchical parsing for semantic segmentation"
    - "conditional random fields as recurrent neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "feedforward semantic segmentation with zoom out features"
    - "deep visual semantic alignments for generating image descriptions"
    - "fully convolutional multi class multiple instance learning"

Connected through hyperedge HE-8 (induced by source paper "scene grammars factor graphs and belief propagation"):
  with members:
    - "multiscale fields of patterns"
    - "learning a hierarchical compositional shape vocabulary for multi class object representation"
    - "loopy belief propagation for approximate inference an empirical study"

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.LG(Machine Learning)