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 revisit of hashing algorithms for approximate nearest neighbor search"
  Abstract: Approximate Nearest Neighbor Search (ANNS) is a fundamental problem in many areas of machine learning and data mining. During the past decade, numerous hashing algorithms are proposed to solve this problem. Every proposed algorithm claims outperform other state-of-the-art hashing methods. However, the evaluation of these hashing papers was not thorough enough, and those claims should be re-examined. The ultimate goal of an ANNS method is returning the most accurate answers (nearest neighbors) in the shortest time. If implemented correctly, almost all the hashing methods will have their performance improved as the code length increases. However, many existing hashing papers only report the performance with the code length shorter than 128. In this paper, we carefully revisit the problem of search with a hash index, and analyze the pros and cons of two popular hash index search procedures. Then we proposed a very simple but effective two level index structures and make a thorough comparison of eleven popular hashing algorithms. Surprisingly, the random-projection-based Locality Sensitive Hashing (LSH) is the best performed algorithm, which is in contradiction to the claims in all the other ten hashing papers. Despite the extreme simplicity of random-projection-based LSH, our results show that the capability of this algorithm has been far underestimated. For the sake of reproducibility, all the codes used in the paper are released on GitHub, which can be used as a testing platform for a fair comparison between various hashing algorithms.

Connected through hyperedge HE-1 (induced by source paper "deep multi index hashing for person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "deep supervised discrete hashing"
    - "in defense of the triplet loss for person re identification"
    - "a revisit on deep hashings for large scale content based image retrieval"
    - "re ranking person re identification with k reciprocal encoding"
    - "stochastic generative hashing"

Connected through hyperedge HE-2 (induced by source paper "unsupervised rank preserving hashing for large scale image retrieval"):
  with members:
    - "link and code fast indexing with graphs and compact regression codes"
    - "polysemous codes"
    - "deep binary reconstruction for cross modal hashing"
    - "patternnet visual pattern mining with deep neural network"
    - "a survey on learning to hash"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "how should we evaluate supervised hashing"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "norm ranging lsh for maximum inner product search"):
  with members:
    - "practical and optimal lsh for angular distance"
    - "optimal data dependent hashing for approximate near neighbors"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"

Connected through hyperedge HE-4 (induced by source paper "a revisit on deep hashings for large scale content based image retrieval"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "transitive hashing network for heterogeneous multimedia retrieval"
    - "deep hashing a joint approach for image signature learning"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "fast approximate nearest neighbor search with navigating spreading out graphs"):
  with members:
    - "billion scale similarity search with gpus"
    - "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "approximate nearest neighbor search on high dimensional data experiments analyses and improvement v1 0"
    - "approximate similarity search for online multimedia services on distributed cpu gpu platforms"

Connected through hyperedge HE-6 (induced by source paper "supervised deep hashing for hierarchical labeled data"):
  with members:
    - "ssdh semi supervised deep hashing for large scale 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"

Connected through hyperedge HE-7 (induced by source paper "evaluation of hashing methods performance on binary feature descriptors"):
  with members:
    - "a survey on learning to hash"
    - "learning to hash for indexing big data a survey"
    - "supervised hashing using graph cuts and boosted decision trees"
    - "hashing for similarity search 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)