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: "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
  Abstract: We present a new approach for the approximate K-nearest neighbor search based on navigable small world graphs with controllable hierarchy (Hierarchical NSW, HNSW). The proposed solution is fully graph-based, without any need for additional search structures, which are typically used at the coarse search stage of the most proximity graph techniques. Hierarchical NSW incrementally builds a multi-layer structure consisting from hierarchical set of proximity graphs (layers) for nested subsets of the stored elements. The maximum layer in which an element is present is selected randomly with an exponentially decaying probability distribution. This allows producing graphs similar to the previously studied Navigable Small World (NSW) structures while additionally having the links separated by their characteristic distance scales. Starting search from the upper layer together with utilizing the scale separation boosts the performance compared to NSW and allows a logarithmic complexity scaling. Additional employment of a heuristic for selecting proximity graph neighbors significantly increases performance at high recall and in case of highly clustered data. Performance evaluation has demonstrated that the proposed general metric space search index is able to strongly outperform previous opensource state-of-the-art vector-only approaches. Similarity of the algorithm to the skip list structure allows straightforward balanced distributed implementation.

Connected through hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review"):
  with members:
    - "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "graph reduction by local variation"
    - "scalable spectral clustering using random binning features"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-2 (induced by source paper "a survey on deep hashing methods"):
  with members:
    - "end to end supervised product quantization for image search and retrieval"
    - "distillhash unsupervised deep hashing by distilling data pairs"
    - "deep triplet quantization"
    - "hadamard codebook based deep hashing"
    - "central similarity hashing via hadamard matrix"
    - "deep attention guided hashing"
    - "deep spherical quantization for image search"
    - "deep ordinal hashing with spatial attention"

Connected through hyperedge HE-3 (induced by source paper "kidnapped radar topological radar localisation using rotationally invariant metric learning"):
  with members:
    - "masking by moving learning distraction free radar odometry from pose information"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "making convolutional networks shift invariant again"
    - "radar only ego motion estimation in difficult settings via graph matching"
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "omnidirectional cnn for visual place recognition and navigation"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "local descriptor for robust place recognition using lidar intensity"

Connected through hyperedge HE-4 (induced by source paper "edit distance embedding using convolutional neural networks"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "practical and optimal lsh for angular distance"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "hashing for similarity search a survey"
    - "pass join a partition based method for similarity joins"

Connected through hyperedge HE-5 (induced by source paper "training neural response selection for task oriented dialogue systems"):
  with members:
    - "passage re ranking with bert"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "simple applications of bert for ad hoc document retrieval"
    - "a repository of conversational datasets"
    - "multi task deep neural networks for natural language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "learning semantic textual similarity from conversations"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"

Connected through hyperedge HE-6 (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"
    - "fast k nearest neighbour search via prioritized dci"

Connected through hyperedge HE-7 (induced by source paper "the fishyscapes benchmark measuring blind spots in semantic segmentation"):
  with members:
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "fusion volumetric object level slam"
    - "semantic foggy scene understanding with synthetic data"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-8 (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"

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)