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: "fast k nearest neighbour search via prioritized dci"
  Abstract: Most exact methods for k-nearest neighbour search suffer from the curse of dimensionality; that is, their query times exhibit exponential dependence on either the ambient or the intrinsic dimensionality. Dynamic Continuous Indexing (DCI) offers a promising way of circumventing the curse and successfully reduces the dependence of query time on intrinsic dimensionality from exponential to sublinear. In this paper, we propose a variant of DCI, which we call Prioritized DCI, and show a remarkable improvement in the dependence of query time on intrinsic dimensionality. In particular, a linear increase in intrinsic dimensionality, or equivalently, an exponential increase in the number of points near a query, can be mostly counteracted with just a linear increase in space. We also demonstrate empirically that Prioritized DCI significantly outperforms prior methods. In particular, relative to Locality-Sensitive Hashing (LSH), Prioritized DCI reduces the number of distance evaluations by a factor of 14 to 116 and the memory consumption by a factor of 21.

Connected through hyperedge HE-1 (induced by source paper "multi step retriever reader interaction for scalable open domain question answering"):
  with members:
    - "training a ranking function for open domain question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "efficient and robust question answering from minimal context over documents"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "emergent communication through negotiation"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "exact and or fast nearest neighbors"):
  with members:
    - "optimization of indexing based on k nearest neighbor graph for proximity search in high dimensional data"
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "billion scale similarity search with gpus"
    - "key value memory networks for directly reading documents"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-3 (induced by source paper "a new burrows wheeler transform markov distance"):
  with members:
    - "toward metric indexes for incremental insertion and querying"
    - "lempel ziv jaccard distance an effective alternative to ssdeep and sdhash"
    - "on normalized compression distance and large malware"
    - "the similarity metric"

Connected through hyperedge HE-4 (induced by source paper "implicit maximum likelihood estimation"):
  with members:
    - "the numerics of gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "do gans actually learn the distribution an empirical study"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "energy based generative adversarial network"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "improved techniques for training gans"

Connected through hyperedge HE-5 (induced by source paper "super resolution via conditional implicit maximum likelihood estimation"):
  with members:
    - "implicit maximum likelihood estimation"
    - "do gans actually learn the distribution an empirical study"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "super resolution with deep convolutional sufficient statistics"
    - "fast k nearest neighbour search via dynamic continuous indexing"
    - "image super resolution using deep convolutional networks"
    - "intriguing properties of neural networks"

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)