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: "supervised hashing using graph cuts and boosted decision trees"
  Abstract: To build large-scale query-by-example image retrieval systems, embedding image features into a binary Hamming space provides great benefits. Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the binary Hamming space. Most existing approaches apply a single form of hash function, and an optimization process which is typically deeply coupled to this specific form. This tight coupling restricts the flexibility of those methods, and can result in complex optimization problems that are difficult to solve. In this work we proffer a flexible yet simple framework that is able to accommodate different types of loss functions and hash functions. The proposed framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problem-specific hashing methods. Our framework decomposes the hashing learning problem into two steps: binary code (hash bit) learning and hash function learning. The first step can typically be formulated as binary quadratic problems, and the second step can be accomplished by training a standard binary classifier. For solving large-scale binary code inference, we show how it is possible to ensure that the binary quadratic problems are submodular such that efficient graph cut methods may be used. To achieve efficiency as well as efficacy on large-scale high-dimensional data, we propose to use boosted decision trees as the hash functions, which are nonlinear, highly descriptive, and are very fast to train and evaluate. Experiments demonstrate that the proposed method significantly outperforms most state-of-the-art methods, especially on high-dimensional data.

Connected through hyperedge HE-1 (induced by source paper "deep ordinal hashing with spatial attention"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "end to end comparative attention networks for person re identification"
    - "deep region hashing for efficient large scale instance search from images"
    - "supervising neural attention models for video captioning by human gaze data"
    - "fooling vision and language models despite localization and attention mechanism"
    - "image captioning with semantic attention"
    - "weakly supervised cascaded convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "semantic cluster unary loss for efficient deep hashing"):
  with members:
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "deep reinforcement learning for image hashing"
    - "mihash online hashing with mutual information"
    - "deep supervised discrete hashing"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "no fuss distance metric learning using proxies"

Connected through hyperedge HE-3 (induced by source paper "a proposal based approach for activity image to video retrieval"):
  with members:
    - "multiple instance learning with graph neural networks"
    - "attention based deep multiple instance learning"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "semi supervised classification with graph convolutional networks"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-4 (induced by source paper "fast supervised discrete hashing"):
  with members:
    - "supervised discrete hashing with relaxation"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "inductive hashing on manifolds"

Connected through hyperedge HE-5 (induced by source paper "supervised discrete hashing with relaxation"):
  with members:
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "sparse principal component analysis via rotation and truncation"
    - "inductive hashing on manifolds"

Connected through hyperedge HE-6 (induced by source paper "query adaptive hash code ranking for large scale multi view visual search"):
  with members:
    - "multi view intact space learning"
    - "projection bank from high dimensional data to medium length binary codes"
    - "hashing on nonlinear manifolds"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "inductive hashing on manifolds"

Connected through hyperedge HE-7 (induced by source paper "exploring auxiliary context discrete semantic transfer hashing for scalable image retrieval"):
  with members:
    - "semisupervised feature analysis by mining correlations among multiple tasks"
    - "learning to hash for indexing big data a survey"
    - "self taught hashing for fast similarity search"

Connected through hyperedge HE-8 (induced by source paper "texture synthesis guided deep hashing for texture image retrieval"):
  with members:
    - "local neighborhood intensity pattern a new texture feature descriptor for image retrieval"
    - "non stationary texture synthesis by adversarial expansion"
    - "efficient nearest neighbors search for large scale landmark recognition"
    - "hashing with binary matrix pursuit"
    - "deep supervised discrete hashing"
    - "texturegan controlling deep image synthesis with texture patches"
    - "fine tuning cnn image retrieval with no human annotation"
    - "hashnet deep learning to hash by continuation"

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)