human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "hinge loss markov random fields and probabilistic soft logic"):
  Abstract: A fundamental challenge in developing high-impact machine learning technologies is balancing the need to model rich, structured domains with the ability to scale to big data. Many important problem areas are both richly structured and large scale, from social and biological networks, to knowledge graphs and the Web, to images, video, and natural language. In this paper, we introduce two new formalisms for modeling structured data, and show that they can both capture rich structure and scale to big data. The first, hinge-loss Markov random fields (HL-MRFs), is a new kind of probabilistic graphical model that generalizes different approaches to convex inference. We unite three approaches from the randomized algorithms, probabilistic graphical models, and fuzzy logic communities, showing that all three lead to the same inference objective. We then define HL-MRFs by generalizing this unified objective. The second new formalism, probabilistic soft logic (PSL), is a probabilistic programming language that makes HL-MRFs easy to define using a syntax based on first-order logic. We introduce an algorithm for inferring most-probable variable assignments (MAP inference) that is much more scalable than general-purpose convex optimization methods, because it uses message passing to take advantage of sparse dependency structures. We then show how to learn the parameters of HL-MRFs. The learned HL-MRFs are as accurate as analogous discrete models, but much more scalable. Together, these algorithms enable HL-MRFs and PSL to model rich, structured data at scales not previously possible.

Includes members:
  Member "reinforcement and imitation learning via interactive no regret learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning hierarchical teaching policies for cooperative agents")
  Member "exploiting social network structure for person to person sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "user profiling using hinge loss markov random fields")
  Member "inferring user preferences by probabilistic logical reasoning over social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sample nlpde and nlode social media modeling of information transmission for infectious diseases case study ebola")
  Member "tighter linear program relaxations for high order graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "worst case polynomial time exact map inference on discrete models with global dependencies")
  Member "hinge loss markov random fields convex inference for structured prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep ordinal regression for pledge specificity prediction")
  Member "a hypergraph partitioned vertex programming approach for large scale consensus optimization":
    also connected through other hyperedges:
      (none)
  Member "object oriented bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic software modeling a data driven paradigm for software analysis")
  Member "spook a system for probabilistic object oriented knowledge representation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hypothesis management in situation specific network construction")

Question: Please tell me which class the source paper 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)