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: "discriminative probabilistic models for relational data"
  Abstract: In many supervised learning tasks, the entities to be labeled are related to each other in complex ways and their labels are not independent. For example, in hypertext classification, the labels of linked pages are highly correlated. A standard approach is to classify each entity independently, ignoring the correlations between them. Recently, Probabilistic Relational Models, a relational version of Bayesian networks, were used to define a joint probabilistic model for a collection of related entities. In this paper, we present an alternative framework that builds on (conditional) Markov networks and addresses two limitations of the previous approach. First, undirected models do not impose the acyclicity constraint that hinders representation of many important relational dependencies in directed models. Second, undirected models are well suited for discriminative training, where we optimize the conditional likelihood of the labels given the features, which generally improves classification accuracy. We show how to train these models effectively, and how to use approximate probabilistic inference over the learned model for collective classification of multiple related entities. We provide experimental results on a webpage classification task, showing that accuracy can be significantly improved by modeling relational dependencies.

Connected through hyperedge HE-1 (induced by source paper "learning distributional programs for relational autocompletion"):
  with members:
    - "learning probabilistic logic programs in continuous domains"
    - "bayes ball the rational pastime for determining irrelevance and requisite information in belief networks and influence diagrams"
    - "context specific independence in bayesian networks"
    - "say em for selecting probabilistic models for logical sequences"
    - "lifted inference for relational continuous models"

Connected through hyperedge HE-2 (induced by source paper "attributed graph learning with 2 d graph convolution"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "gmnn graph markov neural networks"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "attributed graph clustering via adaptive graph convolution"
    - "learning discrete structures for graph neural networks"
    - "label efficient semi supervised learning via graph filtering"
    - "a flexible generative framework for graph based semi supervised learning"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-3 (induced by source paper "video relationship reasoning using gated spatio temporal energy graph"):
  with members:
    - "context dependent diffusion network for visual relationship detection"
    - "discovering order in unordered datasets generative markov networks"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "videos as space time region graphs"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "attention is all you need"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-4 (induced by source paper "probabilistic logic neural networks for reasoning"):
  with members:
    - "reasoning visual dialogs with structural and partial observations"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "gmnn graph markov neural networks"
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "improving knowledge graph embedding using simple constraints"
    - "learning human object interactions by graph parsing neural networks"
    - "m walk learning to walk over graphs using monte carlo tree search"

Connected through hyperedge HE-5 (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"
    - "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"
    - "complexity of inference in graphical models"

Connected through hyperedge HE-6 (induced by source paper "gmnn graph markov neural networks"):
  with members:
    - "inference in probabilistic graphical models by graph neural networks"
    - "neural message passing for quantum chemistry"
    - "convolutional 2d knowledge graph embeddings"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "discriminative embeddings of latent variable models for structured data"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "mebn rm a mapping between multi entity bayesian network and relational model"):
  with members:
    - "the ctu prague relational learning repository"
    - "relational bayesian networks"

Connected through hyperedge HE-8 (induced by source paper "context based object viewpoint estimation a 2d relational approach"):
  with members:
    - "active object localization in visual situations"
    - "recovering hard to find object instances by sampling context based object proposals"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "viewpoints and keypoints"

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)