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: "large scale binary quadratic optimization using semidefinite relaxation and applications"
  Abstract: In computer vision, many problems can be formulated as binary quadratic programs (BQPs), which are in general NP hard. Finding a solution when the problem is of large size to be of practical interest typically requires relaxation. Semidefinite relaxation usually yields tight bounds, but its computational complexity is high. In this work, we present a semidefinite programming (SDP) formulation for BQPs, with two desirable properties. First, it produces similar bounds to the standard SDP formulation. Second, compared with the conventional SDP formulation, the proposed SDP formulation leads to a considerably more efficient and scalable dual optimization approach. We then propose two solvers, namely, quasi-Newton and smoothing Newton methods, for the simplified dual problem. Both of them are significantly more efficient than standard interior-point methods. Empirically the smoothing Newton solver is faster than the quasi-Newton solver for dense or medium-sized problems, while the quasi-Newton solver is preferable for large sparse/structured problems.

Connected through hyperedge HE-1 (induced by source paper "hashing with binary matrix pursuit"):
  with members:
    - "hashing with mutual information"
    - "mihash online hashing with mutual information"
    - "hashnet deep learning to hash by continuation"
    - "fast training of triplet based deep binary embedding networks"
    - "learning of generalized low rank models a greedy approach"
    - "deep supervised hashing with triplet labels"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"
    - "a survey on learning to hash"

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"
    - "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"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"

Connected through hyperedge HE-3 (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"
    - "improving image restoration with soft rounding"
    - "a fast semidefinite approach to solving binary quadratic problems"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-4 (induced by source paper "binary hashing with semidefinite relaxation and augmented lagrangian"):
  with members:
    - "hashing with binary autoencoders"
    - "hashing for similarity search a survey"
    - "caffe convolutional architecture for fast feature embedding"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "a general two step approach to learning based hashing"

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)