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: "mihash online hashing with mutual information"
  Abstract: Learning-based hashing methods are widely used for nearest neighbor retrieval, and recently, online hashing methods have demonstrated good performance-complexity trade-offs by learning hash functions from streaming data. In this paper, we first address a key challenge for online hashing: the binary codes for indexed data must be recomputed to keep pace with updates to the hash functions. We propose an efficient quality measure for hash functions, based on an information-theoretic quantity, mutual information, and use it successfully as a criterion to eliminate unnecessary hash table updates. Next, we also show how to optimize the mutual information objective using stochastic gradient descent. We thus develop a novel hashing method, MIHash, that can be used in both online and batch settings. Experiments on image retrieval benchmarks (including a 2.5M image dataset) confirm the effectiveness of our formulation, both in reducing hash table recomputations and in learning high-quality hash functions.

Connected through hyperedge HE-1 (induced by source paper "learning with average precision training image retrieval with a listwise loss"):
  with members:
    - "local descriptors optimized for average precision"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "vse improving visual semantic embeddings with hard negatives"
    - "fine tuning cnn image retrieval with no human annotation"
    - "no fuss distance metric learning using proxies"
    - "leveraging weakly annotated data for fashion image retrieval and label prediction"

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"
    - "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"
    - "smooth neighbors on teacher graphs for semi supervised learning"

Connected through hyperedge HE-3 (induced by source paper "embarrassingly simple binary representation learning"):
  with members:
    - "zero shot sketch image hashing"
    - "bingan learning compact binary descriptors with a regularized gan"
    - "subic a supervised structured binary code for image search"
    - "binary generative adversarial networks for image retrieval"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "stochastic generative hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"

Connected through hyperedge HE-4 (induced by source paper "deep supervised hashing leveraging quadratic spherical mutual information for content based image retrieval"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "deep supervised discrete hashing"
    - "asymmetric deep supervised hashing"
    - "hashnet deep learning to hash by continuation"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep supervised hashing with triplet labels"
    - "densely connected convolutional networks"
    - "a survey on learning to hash"

Connected through hyperedge HE-5 (induced by source paper "on the possibility of rewarding structure learning agents mutual information on linguistic random sets"):
  with members:
    - "miss tools and mr fruit emergent communication in agents learning about object affordances"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "a survey of reinforcement learning informed by natural language"
    - "diversity is all you need learning skills without a reward function"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "unsupervised sentence representations as word information series revisiting tf idf"
    - "deep reinforcement learning for dialogue generation"
    - "multiresolution recurrent neural networks an application to dialogue response generation"

Connected through hyperedge HE-6 (induced by source paper "online hashing with efficient updating of binary codes"):
  with members:
    - "towards optimal discrete online hashing with balanced similarity"
    - "coupled cyclegan unsupervised hashing network for cross modal retrieval"
    - "binary generative adversarial networks for image retrieval"
    - "a survey on learning to hash"
    - "very deep convolutional networks for large scale image recognition"
    - "microsoft coco common objects in context"
    - "the power of asymmetry in binary hashing"

Connected through hyperedge HE-7 (induced by source paper "supervised online hashing via similarity distribution learning"):
  with members:
    - "towards optimal discrete online hashing with balanced similarity"
    - "hadamard matrix guided online hashing"
    - "ordinal constrained binary code learning for nearest neighbor search"
    - "how should we evaluate supervised hashing"
    - "learning to hash for indexing big data a survey"
    - "very deep convolutional networks for large scale image recognition"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-8 (induced by source paper "supervised online hashing via hadamard codebook learning"):
  with members:
    - "asymmetric deep supervised hashing"
    - "ordinal constrained binary code learning for nearest neighbor search"
    - "a survey on learning to hash"
    - "very deep convolutional networks for large scale image recognition"
    - "simple and deterministic matrix sketching"
    - "solving multiclass learning problems via error correcting output codes"

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)