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: "binary hashing with semidefinite relaxation and augmented lagrangian"
  Abstract: This paper proposes two approaches for inferencing binary codes in two-step (supervised, unsupervised) hashing. We first introduce an unified formulation for both supervised and unsupervised hashing. Then, we cast the learning of one bit as a Binary Quadratic Problem (BQP). We propose two approaches to solve BQP. In the first approach, we relax BQP as a semidefinite programming problem which its global optimum can be achieved. We theoretically prove that the objective value of the binary solution achieved by this approach is well bounded. In the second approach, we propose an augmented Lagrangian based approach to solve BQP directly without relaxing the binary constraint. Experimental results on three benchmark datasets show that our proposed methods compare favorably with the state of the art.

Connected through hyperedge HE-1 (induced by source paper "on device scalable image based localization via prioritized cascade search and fast one many ransac"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "image based localization using hourglass networks"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "selective deep convolutional features for image retrieval"
    - "image based localization using lstms for structured feature correlation"
    - "learning to hash with binary deep neural network"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "metric localization using google street view"

Connected through hyperedge HE-2 (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"
    - "large scale binary quadratic optimization using semidefinite relaxation and applications"
    - "fast training of triplet based deep binary embedding networks"
    - "learning of generalized low rank models a greedy approach"
    - "deep supervised hashing with triplet labels"
    - "a survey on learning to hash"

Connected through hyperedge HE-3 (induced by source paper "simultaneous compression and quantization a joint approach for efficient unsupervised hashing"):
  with members:
    - "simultaneous feature aggregating and hashing for compact binary code learning"
    - "subic a supervised structured binary code for image search"
    - "hashnet deep learning to hash by continuation"
    - "a survey on learning to hash"
    - "learning to hash with binary deep neural network"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "learning to hash for indexing big data a survey"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "compact hash code learning with binary deep neural network"):
  with members:
    - "selective deep convolutional features for image retrieval"
    - "hashnet deep learning to hash by continuation"
    - "embedding based on function approximation for large scale image search"
    - "a survey on learning to hash"
    - "learning to hash with binary deep neural network"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "hashing with binary autoencoders"

Connected through hyperedge HE-5 (induced by source paper "simultaneous feature aggregating and hashing for large scale image search"):
  with members:
    - "learning to hash with binary deep neural network"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "bilinear random projections for locality sensitive binary codes"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "hashing with binary autoencoders"
    - "learning to hash for indexing big data a survey"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"

Connected through hyperedge HE-6 (induced by source paper "a survey on learning to hash"):
  with members:
    - "collaborative quantization for cross modal similarity search"
    - "scalable discrete supervised hash learning with asymmetric matrix factorization"
    - "approximate search with quantized sparse representations"
    - "learning to hash with binary deep neural network"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "hashing with binary autoencoders"
    - "learning to hash for indexing big data a survey"

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)