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: "hashing with mutual information"
  Abstract: Binary vector embeddings enable fast nearest neighbor retrieval in large databases of high-dimensional objects, and play an important role in many practical applications, such as image and video retrieval. We study the problem of learning binary vector embeddings under a supervised setting, also known as hashing. We propose a novel supervised hashing method based on optimizing an information-theoretic quantity, mutual information. We show that optimizing mutual information can reduce ambiguity in the induced neighborhood structure in learned Hamming space, which is essential in obtaining high retrieval performance. To this end, we optimize mutual information in deep neural networks with minibatch stochastic gradient descent, with a formulation that maximally and efficiently utilizes available supervision. Experiments on four image retrieval benchmarks, including ImageNet, confirm the effectiveness of our method in learning high-quality binary embeddings for nearest neighbor retrieval.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep hashing methods"):
  with members:
    - "end to end supervised product quantization for image search and retrieval"
    - "distillhash unsupervised deep hashing by distilling data pairs"
    - "deep triplet quantization"
    - "hadamard codebook based deep hashing"
    - "central similarity hashing via hadamard matrix"
    - "deep attention guided hashing"
    - "deep spherical quantization for image search"
    - "deep ordinal hashing with spatial attention"

Connected through hyperedge HE-2 (induced by source paper "random vlad based deep hashing for efficient image retrieval"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "learning to hash for indexing big data a survey"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "facenet a unified embedding for face recognition and clustering"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-3 (induced by source paper "pairwise teacher student network for semi supervised hashing"):
  with members:
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "deep semantic hashing with generative adversarial networks"
    - "deep supervised discrete hashing"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "hashnet deep learning to hash by continuation"
    - "smooth neighbors on teacher graphs for semi supervised learning"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"

Connected through hyperedge HE-4 (induced by source paper "hadamard codebook based deep hashing"):
  with members:
    - "hadamard matrix guided online hashing"
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"

Connected through hyperedge HE-5 (induced by source paper "hashing with binary matrix pursuit"):
  with members:
    - "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"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"
    - "a survey on learning to hash"

Connected through hyperedge HE-6 (induced by source paper "error correction maximization for deep image hashing"):
  with members:
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "return of the devil in the details delving deep into convolutional nets"
    - "learning hash functions using column generation"

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)