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: "a fast semidefinite approach to solving binary quadratic problems"
  Abstract: Many computer vision problems can be formulated as binary quadratic programs (BQPs). Two classic relaxation methods are widely used for solving BQPs, namely, spectral methods and semi definite programming (SDP), each with their own advantages and disadvantages. Spectral relaxation is simple and easy to implement, but its bound is loose. Semi definite relaxation has a tighter bound, but its computational complexity is high for large scale problems. We present a new SDP formulation for BQPs, with two desirable properties. First, it has a similar relaxation bound to conventional SDP formulations. Second, compared with conventional SDP methods, the new SDP formulation leads to a significantly more efficient and scalable dual optimization approach, which has the same degree of complexity as spectral methods. Extensive experiments on various applications including clustering, image segmentation, co-segmentation and registration demonstrate the usefulness of our SDP formulation for solving large-scale BQPs.

Connected through hyperedge HE-1 (induced by source paper "convex optimisation for inverse kinematics"):
  with members:
    - "reticam real time human performance capture from monocular video"
    - "convex relaxations for pose graph optimization with outliers"
    - "rotation averaging and strong duality"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "real time hand tracking under occlusion from an egocentric rgb d sensor"
    - "non iterative rigid 2d 3d point set registration using semidefinite programming"
    - "near optimal joint object matching via convex relaxation"

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"
    - "scalable semidefinite relaxation for maximum a posterior estimation"
    - "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 "ell_p box admm a versatile framework for integer programming"):
  with members:
    - "tracklet association by online target specific metric learning and coherent dynamics estimation"
    - "large scale binary quadratic optimization using semidefinite relaxation and applications"
    - "improving image restoration with soft rounding"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-5 (induced by source paper "biconvex relaxation for semidefinite programming in computer vision"):
  with members:
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"
    - "a field guide to forward backward splitting with a fasta implementation"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"

Connected through hyperedge HE-6 (induced by source paper "multi view kernel completion"):
  with members:
    - "clustering on multiple incomplete datasets via collective kernel learning"
    - "algorithms for learning kernels based on centered alignment"

Connected through hyperedge HE-7 (induced by source paper "efficient sdp inference for fully connected crfs based on low rank decomposition"):
  with members:
    - "scalable semidefinite relaxation for maximum a posterior estimation"
    - "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-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"
    - "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.CV(Computer Vision and Pattern Recognition)