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 "characterizing distribution equivalence for cyclic and acyclic directed graphs"):
  Abstract: The main way for defining equivalence among acyclic directed graphs is based on the conditional independencies of the distributions that they can generate. However, it is known that when cycles are allowed in the structure, conditional independence is not a suitable notion for equivalence of two structures, as it does not reflect all the information in the distribution that can be used for identification of the underlying structure. In this paper, we present a general, unified notion of equivalence for linear Gaussian directed graphs. Our proposed definition for equivalence is based on the set of distributions that the structure is able to generate. We take a first step towards devising methods for characterizing the equivalence of two structures, which may be cyclic or acyclic. Additionally, we propose a score-based method for learning the structure from observational data.

Includes members:
  Member "a polynomial time algorithm for deciding markov equivalence of directed cyclic graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discovering cyclic causal models by independent components analysis")
  Member "a discovery algorithm for directed cyclis graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data driven root cause analysis for distributed system anomalies")
  Member "learning bayesian networks the combination of knowledge and statistical data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "directed cyclic graphical representations of feedback models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "from random differential equations to structural causal models the stochastic case")
  Member "on the testable implications of causal models with hidden variables":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "ordering based search a simple and effective algorithm for learning bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "solving all regression models for learning gaussian networks using givens rotations")
  Member "discovering cyclic causal models by independent components analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "causal constraints models")

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)