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 general two step approach to learning based hashing"
  Abstract: Most existing approaches to hashing 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 the method to respond to the data, and can result in complex optimization problems that are difficult to solve. Here we propose a flexible yet simple framework that is able to accommodate different types of loss functions and hash functions. This 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 hashing learning problem into two steps: hash bit learning and hash function learning based on the learned bits. The first step can typically be formulated as binary quadratic problems, and the second step can be accomplished by training standard binary classifiers. Both problems have been extensively studied in the literature. Our extensive experiments demonstrate that the proposed framework is effective, flexible and outperforms the state-of-the-art.

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 "feature pyramid hashing"):
  with members:
    - "deep semantic hashing with generative adversarial networks"
    - "deep feature pyramid reconfiguration for object detection"
    - "deep saliency hashing"
    - "adversarial learning for fine grained image search"
    - "hashnet deep learning to hash by continuation"
    - "cascaded pyramid network for multi person pose estimation"
    - "fast training of triplet based deep binary embedding networks"
    - "a survey on learning to hash"

Connected through hyperedge HE-3 (induced by source paper "supervised quantization for similarity search"):
  with members:
    - "hashing on nonlinear manifolds"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "hashing with binary autoencoders"
    - "optimized cartesian k means"
    - "hashing for similarity search a survey"
    - "revisiting kernelized locality sensitive hashing for improved large scale image retrieval"
    - "inductive hashing on manifolds"
    - "fast neighborhood graph search using cartesian concatenation"

Connected through hyperedge HE-4 (induced by source paper "hashing with mutual information"):
  with members:
    - "mihash online hashing with mutual information"
    - "deep hashing network for unsupervised domain adaptation"
    - "stochastic generative hashing"
    - "hashnet deep learning to hash by continuation"
    - "few shot learning through an information retrieval lens"
    - "fast training of triplet based deep binary embedding networks"
    - "deep supervised hashing with triplet labels"
    - "a survey on learning to hash"

Connected through hyperedge HE-5 (induced by source paper "supervised learning of semantics preserving hash via deep convolutional neural networks"):
  with members:
    - "group invariant deep representations for image instance retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "deep residual learning for image recognition"
    - "exploiting local features from deep networks for image retrieval"
    - "aggregating deep convolutional features for image retrieval"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-6 (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"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"

Connected through hyperedge HE-7 (induced by source paper "deep discrete supervised hashing"):
  with members:
    - "global hashing system for fast image search"
    - "deep supervised discrete hashing"
    - "fast training of triplet based deep binary embedding networks"
    - "a survey on learning to hash"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "optimizing affinity based binary hashing using auxiliary coordinates"
    - "learning to hash for indexing big data a survey"

Connected through hyperedge HE-8 (induced by source paper "self taught convolutional neural networks for short text clustering"):
  with members:
    - "end to end memory networks"
    - "skip thought vectors"
    - "self adaptive hierarchical sentence model"
    - "unsupervised learning of visual representations using videos"
    - "renet a recurrent neural network based alternative to convolutional networks"
    - "neural responding machine for short text conversation"
    - "deep sentence embedding using the long short term memory networks"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"

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)