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: "hinge loss markov random fields convex inference for structured prediction"
  Abstract: Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable. Instead of working in a combinatorial space, we use hinge-loss Markov random fields (HL-MRFs), an expressive class of graphical models with log-concave density functions over continuous variables, which can represent confidences in discrete predictions. This paper demonstrates that HL-MRFs are general tools for fast and accurate structured prediction. We introduce the first inference algorithm that is both scalable and applicable to the full class of HL-MRFs, and show how to train HL-MRFs with several learning algorithms. Our experiments show that HL-MRFs match or surpass the predictive performance of state-of-the-art methods, including discrete models, in four application domains.

Connected through hyperedge HE-1 (induced by source paper "deep ordinal regression for pledge specificity prediction"):
  with members:
    - "target based speech act classification in political campaign text"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "deep contextualized word representations"
    - "hierarchical structured model for fine to coarse manifesto text analysis"
    - "semi supervised sequence modeling with cross view training"
    - "predicting specificity in classroom discussion"
    - "deep label distribution learning with label ambiguity"

Connected through hyperedge HE-2 (induced by source paper "modeling drug disease relations with linguistic and knowledge graph constraints"):
  with members:
    - "exploiting social network structure for person to person sentiment analysis"
    - "probabilistic similarity logic"

Connected through hyperedge HE-3 (induced by source paper "embedding uncertain knowledge graphs"):
  with members:
    - "on2vec embedding based relation prediction for ontology population"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "knowledge base completion baselines strike back"
    - "convolutional 2d knowledge graph embeddings"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "proje embedding projection for knowledge graph completion"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-4 (induced by source paper "on a convex logic fragment for learning and reasoning"):
  with members:
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "hinge loss markov random fields and probabilistic soft logic"
    - "probabilistic similarity logic"

Connected through hyperedge HE-5 (induced by source paper "estimating accuracy from unlabeled data a probabilistic logic approach"):
  with members:
    - "hinge loss markov random fields and probabilistic soft logic"
    - "probabilistic similarity logic"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-6 (induced by source paper "answering image riddles using vision and reasoning through probabilistic soft logic"):
  with members:
    - "what value do explicit high level concepts have in vision to language problems"
    - "vqa visual question answering"
    - "deep residual learning for image recognition"
    - "learning to answer questions from image using convolutional neural network"
    - "ask your neurons a neural based approach to answering questions about images"
    - "are you talking to a machine dataset and methods for multilingual image question answering"
    - "microsoft coco common objects in context"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "hinge loss markov random fields and probabilistic soft logic"):
  with members:
    - "reinforcement and imitation learning via interactive no regret learning"
    - "exploiting social network structure for person to person sentiment analysis"
    - "inferring user preferences by probabilistic logical reasoning over social networks"
    - "tighter linear program relaxations for high order graphical models"
    - "a hypergraph partitioned vertex programming approach for large scale consensus optimization"
    - "object oriented bayesian networks"
    - "spook a system for probabilistic object oriented knowledge representation"
    - "algebraic model counting"

Connected through hyperedge HE-8 (induced by source paper "representing meaning with a combination of logical and distributional models"):
  with members:
    - "skip thought vectors"
    - "compositional semantic parsing on semi structured tables"
    - "teaching machines to read and comprehend"
    - "the frobenius anatomy of word meanings i subject and object relative pronouns"
    - "efficient estimation of word representations in vector space"
    - "probabilistic similarity logic"
    - "iterative join graph propagation"
    - "probabilistic theorem proving"

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)