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 "information theoretic inference of common ancestors"):
  Abstract: A directed acyclic graph (DAG) partially represents the conditional independence structure among observations of a system if the local Markov condition holds, that is, if every variable is independent of its non-descendants given its parents. In general, there is a whole class of DAGs that represents a given set of conditional independence relations. We are interested in properties of this class that can be derived from observations of a subsystem only. To this end, we prove an information theoretic inequality that allows for the inference of common ancestors of observed parts in any DAG representing some unknown larger system. More explicitly, we show that a large amount of dependence in terms of mutual information among the observations implies the existence of a common ancestor that distributes this information. Within the causal interpretation of DAGs our result can be seen as a quantitative extension of Reichenbach's Principle of Common Cause to more than two variables. Our conclusions are valid also for non-probabilistic observations such as binary strings, since we state the proof for an axiomatized notion of mutual information that includes the stochastic as well as the algorithmic version.

Includes members:
  Member "quantifying unique information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an operational information decomposition via synergistic disclosure")
  Member "quantifying synergistic mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an operational information decomposition via synergistic disclosure")
  Member "discovering structure in high dimensional data through correlation explanation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "correlation congruence for knowledge distillation")
  Member "bivariate measure of redundant information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an operational information decomposition via synergistic disclosure")
  Member "entropic inequalities and marginal problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "minimum entropy couplings and their applications")
  Member "causal networks semantics and expressiveness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "on the testability of causal models with latent and instrumental variables":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "identifying independencies in causal graphs with feedback")
  Member "nonnegative decomposition of multivariate information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an operational information decomposition via synergistic disclosure")

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.IT(Information Theory)