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: "compact hyperplane hashing with bilinear functions"
  Abstract: Hyperplane hashing aims at rapidly searching nearest points to a hyperplane, and has shown practical impact in scaling up active learning with SVMs. Unfortunately, the existing randomized methods need long hash codes to achieve reasonable search accuracy and thus suffer from reduced search speed and large memory overhead. To this end, this paper proposes a novel hyperplane hashing technique which yields compact hash codes. The key idea is the bilinear form of the proposed hash functions, which leads to higher collision probability than the existing hyperplane hash functions when using random projections. To further increase the performance, we propose a learning based framework in which the bilinear functions are directly learned from the data. This results in short yet discriminative codes, and also boosts the search performance over the random projection based solutions. Large-scale active learning experiments carried out on two datasets with up to one million samples demonstrate the overall superiority of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "distillhash unsupervised deep hashing by distilling data pairs"):
  with members:
    - "fast supervised discrete hashing"
    - "shared predictive cross modal deep quantization"
    - "triplet based deep hashing network for cross modal retrieval"
    - "coupled cyclegan unsupervised hashing network for cross modal retrieval"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "zero shot sketch image hashing"
    - "evolutionary generative adversarial networks"
    - "masking a new perspective of noisy supervision"

Connected through hyperedge HE-2 (induced by source paper "shared predictive cross modal deep quantization"):
  with members:
    - "query adaptive hash code ranking for large scale multi view visual search"
    - "fast supervised discrete hashing"
    - "supervised discrete hashing with relaxation"
    - "collaborative quantization for cross modal similarity search"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "deep cross modal hashing"

Connected through hyperedge HE-3 (induced by source paper "global hashing system for fast image search"):
  with members:
    - "hashing on nonlinear manifolds"
    - "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-4 (induced by source paper "distance sensitive hashing"):
  with members:
    - "approximate furthest neighbor with application to annulus query"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "sub linear privacy preserving near neighbor search with untrusted server on large scale datasets"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "on the complexity of inner product similarity join"
    - "set similarity search beyond minhash"

Connected through hyperedge HE-5 (induced by source paper "set to set hashing with applications in visual recognition"):
  with members:
    - "learning to hash for indexing big data a survey"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "hashing for similarity search a survey"
    - "a general two step approach to learning based hashing"
    - "multimodal similarity preserving hashing"
    - "multi instance learning by treating instances as non i i d samples"

Connected through hyperedge HE-6 (induced by source paper "a survey on learning to hash"):
  with members:
    - "collaborative quantization for cross modal similarity search"
    - "scalable discrete supervised hash learning with asymmetric matrix factorization"
    - "approximate search with quantized sparse representations"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"
    - "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"
    - "hashing with binary autoencoders"

Connected through hyperedge HE-7 (induced by source paper "ordinal constrained binary code learning for nearest neighbor search"):
  with members:
    - "learning to hash for indexing big data a survey"
    - "optimizing ranking measures for compact binary code learning"
    - "learning hash functions using column generation"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-8 (induced by source paper "learning to hash for indexing big data a survey"):
  with members:
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "compressing neural networks with the hashing trick"
    - "learning hash functions using column generation"
    - "inductive hashing on manifolds"
    - "a general two step approach to learning based hashing"
    - "sparse similarity preserving hashing"
    - "on the difficulty of nearest neighbor search"

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)