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: "gaussian belief propagation theory and application"
  Abstract: The canonical problem of solving a system of linear equations arises in numerous contexts in information theory, communication theory, and related fields. In this contribution, we develop a solution based upon Gaussian belief propagation (GaBP) that does not involve direct matrix inversion. The iterative nature of our approach allows for a distributed message-passing implementation of the solution algorithm. In the first part of this thesis, we address the properties of the GaBP solver. We characterize the rate of convergence, enhance its message-passing efficiency by introducing a broadcast version, discuss its relation to classical solution methods including numerical examples. We present a new method for forcing the GaBP algorithm to converge to the correct solution for arbitrary column dependent matrices. #R##N#In the second part we give five applications to illustrate the applicability of the GaBP algorithm to very large computer networks: Peer-to-Peer rating, linear detection, distributed computation of support vector regression, efficient computation of Kalman filter and distributed linear programming. Using extensive simulations on up to 1,024 CPUs in parallel using IBM Bluegene supercomputer we demonstrate the attractiveness and applicability of the GaBP algorithm, using real network topologies with up to millions of nodes and hundreds of millions of communication links. We further relate to several other algorithms and explore their connection to the GaBP algorithm.

Connected through hyperedge HE-1 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "graph partition neural networks for semi supervised classification"
    - "fast generation for convolutional autoregressive models"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-2 (induced by source paper "a multilayer based framework for online background subtraction with freely moving cameras"):
  with members:
    - "it s moving a probabilistic model for causal motion segmentation in moving camera videos"
    - "coherent motion segmentation in moving camera videos using optical flow orientations"

Connected through hyperedge HE-3 (induced by source paper "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"):
  with members:
    - "low complexity near optimal signal detection for uplink large scale mimo systems"
    - "large system analysis of cognitive radio network via partially projected regularized zero forcing precoding"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "energy efficient sensing and communication of parallel gaussian sources"
    - "par aware large scale multi user mimo ofdm downlink"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "fixing convergence of gaussian belief propagation"

Connected through hyperedge HE-4 (induced by source paper "on the convergence of approximate message passing with arbitrary matrices"):
  with members:
    - "learning and free energies for vector approximate message passing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"
    - "variational free energies for compressed sensing"
    - "on convergence of approximate message passing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"

Connected through hyperedge HE-5 (induced by source paper "distributed cross layer optimization in wireless networks a second order approach"):
  with members:
    - "a distributed newton approach for joint multi hop routing and flow control theory and algorithm"
    - "distributed large scale network utility maximization"
    - "polynomial linear programming with gaussian belief propagation"

Connected through hyperedge HE-6 (induced by source paper "sum capacity of interference channels with a local view impact of distributed decisions"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "the approximate capacity of the many to one and one to many gaussian interference channels"
    - "on the sum capacity of a class of cyclically symmetric deterministic interference channels"
    - "the two user gaussian interference channel a deterministic view"
    - "generalized degrees of freedom of the symmetric gaussian k user interference channel"
    - "gaussian interference channel capacity to within one bit"

Connected through hyperedge HE-7 (induced by source paper "properties of bethe free energies and message passing in gaussian models"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "bounds on the bethe free energy for gaussian networks"
    - "graph zeta function in the bethe free energy and loopy belief propagation"
    - "fixing convergence of gaussian belief propagation"

Connected through hyperedge HE-8 (induced by source paper "kernel belief propagation"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "multi label prediction via compressed sensing"

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.IT(Information Theory)