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 greedy approach for budgeted maximum inner product search"
  Abstract: Maximum Inner Product Search (MIPS) is an important task in many machine learning applications such as the prediction phase of a low-rank matrix factorization model for a recommender system. There have been some works on how to perform MIPS in sub-linear time recently. However, most of them do not have the flexibility to control the trade-off between search efficient and search quality. In this paper, we study the MIPS problem with a computational budget. By carefully studying the problem structure of MIPS, we develop a novel Greedy-MIPS algorithm, which can handle budgeted MIPS by design. While simple and intuitive, Greedy-MIPS yields surprisingly superior performance compared to state-of-the-art approaches. As a specific example, on a candidate set containing half a million vectors of dimension 200, Greedy-MIPS runs 200x faster than the naive approach while yielding search results with the top-5 precision greater than 75\%.

Connected through hyperedge HE-1 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the dimensionality of word embedding"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "attention is all you need"
    - "multilingual hierarchical attention networks for document classification"
    - "a read write memory network for movie story understanding"

Connected through hyperedge HE-2 (induced by source paper "learning to route in similarity graphs"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "super convergence very fast training of residual networks using large learning rates"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "sequence to sequence learning as beam search optimization"
    - "generative adversarial imitation learning"
    - "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "interaction hard thresholding consistent sparse quadratic regression in sub quadratic time and space"):
  with members:
    - "high dimensional robust sparse regression"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "distributed pcp theorems for hardness of approximation in p"
    - "nonconvex sparse learning via stochastic optimization with progressive variance reduction"
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "sparse polynomial learning and graph sketching"
    - "compressed matrix multiplication"

Connected through hyperedge HE-4 (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"
    - "quantization based fast 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-5 (induced by source paper "local orthogonal decomposition for maximum inner product search"):
  with members:
    - "billion scale similarity search with gpus"
    - "learning spread out local feature descriptors"
    - "bolt accelerated data mining with fast vector compression"
    - "stacked quantizers for compositional vector compression"

Connected through hyperedge HE-6 (induced by source paper "revisiting wedge sampling for budgeted maximum inner product search"):
  with members:
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "imagenet large scale visual recognition challenge"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-7 (induced by source paper "learning to screen for fast softmax inference on large vocabulary neural networks"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "deep reinforcement learning for dialogue generation"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "a neural attention model for abstractive sentence summarization"
    - "on using very large target vocabulary for neural machine translation"

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.DS(Data Structures and Algorithms)