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: "towards optimal discrete online hashing with balanced similarity"
  Abstract: When facing large-scale image datasets, online hashing serves as a promising solution for online retrieval and prediction tasks. It encodes the online streaming data into compact binary codes, and simultaneously updates the hash functions to renew codes of the existing dataset. To this end, the existing methods update hash functions solely based on the new data batch, without investigating the correlation between such new data and the existing dataset. In addition, existing works update the hash functions using a relaxation process in its corresponding approximated continuous space. And it remains as an open problem to directly apply discrete optimizations in online hashing. In this paper, we propose a novel supervised online hashing method, termed Balanced Similarity for Online Discrete Hashing (BSODH), to solve the above problems in a unified framework. BSODH employs a well-designed hashing algorithm to preserve the similarity between the streaming data and the existing dataset via an asymmetric graph regularization. We further identify the "data-imbalance" problem brought by the constructed asymmetric graph, which restricts the application of discrete optimization in our problem. Therefore, a novel balanced similarity is further proposed, which uses two equilibrium factors to balance the similar and dissimilar weights and eventually enables the usage of discrete optimizations. Extensive experiments conducted on three widely-used benchmarks demonstrate the advantages of the proposed method over the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "ssah semi supervised adversarial deep hashing with self paced hard sample generation"):
  with members:
    - "deep triplet quantization"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "deep semantic hashing with generative adversarial networks"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deep saliency hashing"
    - "ganimation anatomically aware facial animation from a single image"
    - "random erasing data augmentation"
    - "a fast rcnn hard positive generation via adversary for object detection"

Connected through hyperedge HE-2 (induced by source paper "similarity dt kernel similarity embedding for dynamic texture synthesis"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "semi supervised feature learning for improving writer identification"
    - "learning energy based spatial temporal generative convnets for dynamic patterns"
    - "in defense of the triplet loss for person re identification"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "photographic image synthesis with cascaded refinement networks"
    - "temporal generative adversarial nets with singular value clipping"
    - "learning temporal transformations from time lapse videos"

Connected through hyperedge HE-3 (induced by source paper "online hashing with efficient updating of binary codes"):
  with members:
    - "coupled cyclegan unsupervised hashing network for cross modal retrieval"
    - "mihash online hashing with mutual information"
    - "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-4 (induced by source paper "supervised online hashing via similarity distribution learning"):
  with members:
    - "hadamard matrix guided online hashing"
    - "mihash online hashing with mutual information"
    - "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-5 (induced by source paper "hadamard matrix guided online hashing"):
  with members:
    - "fast supervised discrete hashing"
    - "shared predictive cross modal deep quantization"
    - "a survey on learning to hash"
    - "very deep convolutional networks for large scale image recognition"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-6 (induced by source paper "efficient querying from weighted binary codes"):
  with members:
    - "coupled cyclegan unsupervised hashing network for cross modal retrieval"

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.IR(Information Retrieval)