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: "scalable semidefinite relaxation for maximum a posterior estimation"
  Abstract: Maximum a posteriori (MAP) inference over discrete Markov random fields is a fundamental task spanning a wide spectrum of real-world applications, which is known to be NP-hard for general graphs. In this paper, we propose a novel semidefinite relaxation formulation (referred to as SDR) to estimate the MAP assignment. Algorithmically, we develop an accelerated variant of the alternating direction method of multipliers (referred to as SDPAD-LR) that can effectively exploit the special structure of the new relaxation. Encouragingly, the proposed procedure allows solving SDR for large-scale problems, e.g., problems on a grid graph comprising hundreds of thousands of variables with multiple states per node. Compared with prior SDP solvers, SDPAD-LR is capable of attaining comparable accuracy while exhibiting remarkably improved scalability, in contrast to the commonly held belief that semidefinite relaxation can only been applied on small-scale MRF problems. We have evaluated the performance of SDR on various benchmark datasets including OPENGM2 and PIC in terms of both the quality of the solutions and computation time. Experimental results demonstrate that for a broad class of problems, SDPAD-LR outperforms state-of-the-art algorithms in producing better MAP assignment in an efficient manner.

Connected through hyperedge HE-1 (induced by source paper "path invariant map networks"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "robust rotation synchronization via low rank and sparse matrix decomposition"
    - "scannet richly annotated 3d reconstructions of indoor scenes"
    - "data distillation towards omni supervised learning"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "dualgan unsupervised dual learning for image to image translation"
    - "semantic scene completion from a single depth image"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"

Connected through hyperedge HE-2 (induced by source paper "accelerated inference in markov random fields via smooth riemannian optimization"):
  with members:
    - "bonnet an open source training and deployment framework for semantic segmentation in robotics using cnns"
    - "large scale binary quadratic optimization using semidefinite relaxation and applications"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a survey of semantic segmentation"
    - "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation"
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"

Connected through hyperedge HE-3 (induced by source paper "large scale binary quadratic optimization using semidefinite relaxation and applications"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "a fast semidefinite approach to solving binary quadratic problems"
    - "a hybrid algorithm for convex semidefinite optimization"
    - "opengm a c library for discrete graphical models"
    - "tightening lp relaxations for map using message passing"
    - "efficiently searching for frustrated cycles in map inference"

Connected through hyperedge HE-4 (induced by source paper "the projected power method an efficient algorithm for joint alignment from pairwise differences"):
  with members:
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "fast algorithms for robust pca via gradient descent"
    - "f divergence inequalities"
    - "community recovery in graphs with locality"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"

Connected through hyperedge HE-5 (induced by source paper "efficient sdp inference for fully connected crfs based on low rank decomposition"):
  with members:
    - "a fast semidefinite approach to solving binary quadratic problems"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "sharp analysis of low rank kernel matrix approximations"

Connected through hyperedge HE-6 (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"
    - "a fast semidefinite approach to solving binary quadratic problems"
    - "higher order segmentation via multicuts"

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.LG(Machine Learning)