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: "reconstruction of markov random fields from samples some easy observations and algorithms"
  Abstract: Markov random fields are used to model high dimensional distributions in a number of applied areas. Much recent interest has been devoted to the reconstruction of the dependency structure from independent samples from the Markov random fields. We analyze a simple algorithm for reconstructing the underlying graph defining a Markov random field on $n$ nodes and maximum degree $d$ given observations. We show that under mild non-degeneracy conditions it reconstructs the generating graph with high probability using $\Theta(d \epsilon^{-2}\delta^{-4} \log n)$ samples where $\epsilon,\delta$ depend on the local interactions. For most local interaction $\eps,\delta$ are of order $\exp(-O(d))$. #R##N#Our results are optimal as a function of $n$ up to a multiplicative constant depending on $d$ and the strength of the local interactions. Our results seem to be the first results for general models that guarantee that {\em the} generating model is reconstructed. Furthermore, we provide explicit $O(n^{d+2} \epsilon^{-2}\delta^{-4} \log n)$ running time bound. In cases where the measure on the graph has correlation decay, the running time is $O(n^2 \log n)$ for all fixed $d$. We also discuss the effect of observing noisy samples and show that as long as the noise level is low, our algorithm is effective. On the other hand, we construct an example where large noise implies non-identifiability even for generic noise and interactions. Finally, we briefly show that in some simple cases, models with hidden nodes can also be recovered.

Connected through hyperedge HE-1 (induced by source paper "efficiently learning ising models on arbitrary graphs"):
  with members:
    - "hardness of parameter estimation in graphical models"
    - "analysis of boolean functions"
    - "maximum likelihood bounded tree width markov networks"
    - "moment matching polynomials"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"
    - "on learning discrete graphical models using greedy methods"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-2 (induced by source paper "learning graphical models from the glauber dynamics"):
  with members:
    - "hardness of parameter estimation in graphical models"
    - "maximum likelihood bounded tree width markov networks"
    - "on learning discrete graphical models using greedy methods"
    - "uncovering the temporal dynamics of diffusion networks"
    - "topology discovery of sparse random graphs with few participants"
    - "on the convexity of latent social network inference"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-3 (induced by source paper "inverse problems in approximate uniform generation"):
  with members:
    - "learning poisson binomial distributions"
    - "learning k modal distributions via testing"
    - "noise tolerant learning the parity problem and the statistical query model"

Connected through hyperedge HE-4 (induced by source paper "high dimensional gaussian graphical model selection walk summability and local separation criterion"):
  with members:
    - "message passing algorithms reparameterizations and splittings"
    - "counting in graph covers a combinatorial characterization of the bethe entropy function"
    - "topology discovery of sparse random graphs with few participants"
    - "message passing for maximum weight independent set"
    - "max product for maximum weight matching convergence correctness and lp duality"
    - "sufficient conditions for convergence of the sum product algorithm"

Connected through hyperedge HE-5 (induced by source paper "topology discovery of sparse random graphs with few participants"):
  with members:
    - "walking on a graph with a magnifying glass stratified sampling via weighted random walks"
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"
    - "dimes let the internet measure itself"

Connected through hyperedge HE-6 (induced by source paper "on learning discrete graphical models using greedy methods"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-7 (induced by source paper "learning high dimensional markov forest distributions analysis of error rates"):
  with members:
    - "on the number of samples needed to learn the correct structure of a bayesian network"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

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.CC(Computational Complexity)