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 "learning graphical models from the glauber dynamics"):
  Abstract: In this paper we consider the problem of learning undirected graphical models from data generated according to the Glauber dynamics. The Glauber dynamics is a Markov chain that sequentially updates individual nodes (variables) in a graphical model and it is frequently used to sample from the stationary distribution (to which it converges given sufficient time). Additionally, the Glauber dynamics is a natural dynamical model in a variety of settings. This work deviates from the standard formulation of graphical model learning in the literature, where one assumes access to i.i.d. samples from the distribution. #R##N#Much of the research on graphical model learning has been directed towards finding algorithms with low computational cost. As the main result of this work, we establish that the problem of reconstructing binary pairwise graphical models is computationally tractable when we observe the Glauber dynamics. Specifically, we show that a binary pairwise graphical model on $p$ nodes with maximum degree $d$ can be learned in time $f(d)p^2\log p$, for a function $f(d)$, using nearly the information-theoretic minimum number of samples.

Includes members:
  Member "hardness of parameter estimation in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain liftability of relational marginal polytopes")
  Member "maximum likelihood bounded tree width markov networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient learning of optimal markov network topology with k tree modeling")
  Member "on learning discrete graphical models using greedy methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privately learning markov random fields")
  Member "uncovering the temporal dynamics of diffusion networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "topology discovery of sparse random graphs with few participants":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reconstruction of c c channel for structured p2p botnet")
  Member "on the convexity of latent social network inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "information theoretic limits of selecting binary graphical models in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "privately learning markov random fields")
  Member "reconstruction of markov random fields from samples some easy observations and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficiently learning ising models on arbitrary graphs")

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)