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: "norm product belief propagation primal dual message passing for approximate inference"
  Abstract: In this paper we treat both forms of probabilistic inference, estimating marginal probabilities of the joint distribution and finding the most probable assignment, through a unified message-passing algorithm architecture. We generalize the Belief Propagation (BP) algorithms of sum-product and max-product and tree-rewaighted (TRW) sum and max product algorithms (TRBP) and introduce a new set of convergent algorithms based on "convex-free-energy" and Linear-Programming (LP) relaxation as a zero-temprature of a convex-free-energy. The main idea of this work arises from taking a general perspective on the existing BP and TRBP algorithms while observing that they all are reductions from the basic optimization formula of $f + \sum_i h_i$ where the function $f$ is an extended-valued, strictly convex but non-smooth and the functions $h_i$ are extended-valued functions (not necessarily convex). We use tools from convex duality to present the "primal-dual ascent" algorithm which is an extension of the Bregman successive projection scheme and is designed to handle optimization of the general type $f + \sum_i h_i$. Mapping the fractional-free-energy variational principle to this framework introduces the "norm-product" message-passing. Special cases include sum-product and max-product (BP algorithms) and the TRBP algorithms. When the fractional-free-energy is set to be convex (convex-free-energy) the norm-product is globally convergent for estimating of marginal probabilities and for approximating the LP-relaxation. We also introduce another branch of the norm-product, the "convex-max-product". The convex-max-product is convergent (unlike max-product) and aims at solving the LP-relaxation.

Connected through hyperedge HE-1 (induced by source paper "scalable explanation of inferences on large graphs"):
  with members:
    - "an evaluation of the human interpretability of explanation"
    - "efficient search for diverse coherent explanations"
    - "smoke screener or straight shooter detecting elite sybil attacks in user review social networks"
    - "predicting multicellular function through multi layer tissue networks"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "image labeling based on graphical models using wasserstein messages and geometric assignment"):
  with members:
    - "opengm a c library for discrete graphical models"
    - "continuous multiclass labeling approaches and algorithms"

Connected through hyperedge HE-3 (induced by source paper "newton type methods for inference in higher order markov random fields"):
  with members:
    - "a new look at reweighted message passing"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "convergent message passing algorithms a unifying view"
    - "tightening lp relaxations for map using message passing"
    - "an embarrassingly simple speed up of belief propagation with robust potentials"

Connected through hyperedge HE-4 (induced by source paper "multiple scan data association by convex variational inference"):
  with members:
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"
    - "an efficient variational approximation of the best fitting multi bernoulli filter"
    - "approximate evaluation of marginal association probabilities with belief propagation"
    - "loopy belief propagation for approximate inference an empirical study"
    - "the bethe permanent of a nonnegative matrix"
    - "a new class of upper bounds on the log partition function"
    - "approximating the permanent with fractional belief propagation"

Connected through hyperedge HE-5 (induced by source paper "decomposition bounds for marginal map"):
  with members:
    - "message passing algorithms reparameterizations and splittings"
    - "convergent message passing algorithms for inference over general graphs with convex free energies"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "convergent message passing algorithms a unifying view"
    - "tightening lp relaxations for map using message passing"
    - "join graph based cost shifting schemes"
    - "solving map exactly using systematic search"
    - "a new class of upper bounds on the log partition function"

Connected through hyperedge HE-6 (induced by source paper "a new look at reweighted message passing"):
  with members:
    - "generalized sequential tree reweighted message passing"
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "the power of linear programming for valued csps"
    - "convergent message passing algorithms a unifying view"
    - "tightening lp relaxations for map using message passing"
    - "efficiently searching for frustrated cycles in map inference"
    - "the power of linear programming for valued csps a constructive characterization"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-7 (induced by source paper "learning deep structured models"):
  with members:
    - "conditional random fields as recurrent neural networks"
    - "fully connected deep structured networks"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "simultaneous detection and segmentation"
    - "structured learning via logistic regression"
    - "caffe convolutional architecture for fast feature embedding"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "efficient semidefinite branch and cut for map mrf inference"):
  with members:
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"
    - "crf learning with cnn features for image segmentation"
    - "maximum persistency via iterative relaxed inference with graphical models"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "maximum persistency in energy minimization"
    - "partial optimality by pruning for map inference with general graphical models"
    - "scalable semidefinite relaxation for maximum a posterior estimation"
    - "a fast semidefinite approach to solving binary quadratic problems"

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.AI(Artificial Intelligence)