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: "quantization based fast inner product search"
  Abstract: We propose a quantization based approach for fast approximate Maximum Inner Product Search (MIPS). Each database vector is quantized in multiple subspaces via a set of codebooks, learned directly by minimizing the inner product quantization error. Then, the inner product of a query to a database vector is approximated as the sum of inner products with the subspace quantizers. Different from recently proposed LSH approaches to MIPS, the database vectors and queries do not need to be augmented in a higher dimensional feature space. We also provide a theoretical analysis of the proposed approach, consisting of the concentration results under mild assumptions. Furthermore, if a small sample of example queries is given at the training time, we propose a modified codebook learning procedure which further improves the accuracy. Experimental results on a variety of datasets including those arising from deep neural networks show that the proposed approach significantly outperforms the existing state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "pre training tasks for embedding based large scale retrieval"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "latent retrieval for weakly supervised open domain question answering"
    - "reqa an evaluation for end to end answer retrieval models"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "poly encoders transformer architectures and pre training strategies for fast and accurate multi sentence scoring"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "stochastic negative mining for learning with large output spaces"

Connected through hyperedge HE-2 (induced by source paper "the space complexity of inner product filters"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "approximate nearest neighbors in limited space"
    - "adaptive mapreduce similarity joins"
    - "practical data dependent metric compression with provable guarantees"
    - "distributed pcp theorems for hardness of approximation in p"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "bolt accelerated data mining with fast vector compression"
    - "scalable and robust set similarity join"

Connected through hyperedge HE-3 (induced by source paper "neural naturalist generating fine grained image comparisons"):
  with members:
    - "robust change captioning"
    - "expressing visual relationships via language"
    - "learning to describe differences between pairs of similar images"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "attention is all you need"
    - "context aware captions from context agnostic supervision"
    - "the inaturalist species classification and detection dataset"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-4 (induced by source paper "learning dense representations for entity retrieval"):
  with members:
    - "end to end retrieval in continuous space"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "efficient natural language response suggestion for smart reply"
    - "learning distributed representations of texts and entities from knowledge base"
    - "named entity disambiguation for noisy text"
    - "deep joint entity disambiguation with local neural attention"
    - "neural cross lingual entity linking"
    - "dynamic entity representations in neural language models"

Connected through hyperedge HE-5 (induced by source paper "new loss functions for fast maximum inner product search"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "billion scale similarity search with gpus"
    - "stochastic generative hashing"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "trained ternary quantization"
    - "practical and optimal lsh for angular distance"
    - "hashing for similarity search a survey"
    - "fast exact search in hamming space with multi index hashing"

Connected through hyperedge HE-6 (induced by source paper "understanding and improving proximity graph based maximum inner product search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "to index or not to index optimizing exact maximum inner product search"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "a greedy approach for budgeted maximum inner product search"
    - "fast neighborhood graph search using cartesian concatenation"
    - "distributed representations of words and phrases and their compositionality"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"

Connected through hyperedge HE-7 (induced by source paper "efficient inner product approximation in hybrid spaces"):
  with members:
    - "deep cross network for ad click predictions"
    - "accelerated nearest neighbor search with quick adc"
    - "bolt accelerated data mining with fast vector compression"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "practical and optimal lsh for angular distance"
    - "distributed representations of sentences and documents"
    - "stacked quantizers for compositional vector compression"

Connected through hyperedge HE-8 (induced by source paper "norm explicit quantization improving vector quantization for maximum inner product search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "link and code fast indexing with graphs and compact regression codes"
    - "billion scale similarity search with gpus"
    - "clustering is efficient for approximate maximum inner product search"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"

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.AI(Artificial Intelligence)