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 note on the triangle inequality for the jaccard distance"
  Abstract: Two simple proofs of the triangle inequality for the Jaccard distance in terms of nonnegative, monotone, submodular functions are given and discussed.

Connected through hyperedge HE-1 (induced by source paper "generalized intersection over union a metric and a loss for bounding box regression"):
  with members:
    - "yolov3 an incremental improvement"
    - "acquisition of localization confidence for accurate object detection"
    - "path aggregation network for instance segmentation"
    - "focal loss for dense object detection"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-2 (induced by source paper "communication efficient jaccard similarity for high performance distributed genome comparisons"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "maximally consistent sampling and the jaccard index of probability distributions"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "mathematical foundations of the graphblas"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "sparse tensor algebra as a parallel programming model"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"
    - "minimizing communication in linear algebra"

Connected through hyperedge HE-3 (induced by source paper "a probabilistic u net for segmentation of ambiguous images"):
  with members:
    - "a variational u net for conditional appearance and shape generation"
    - "improving gans using optimal transport"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "toward multimodal image to image translation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "nips 2016 tutorial generative adversarial networks"
    - "stochastic multiple choice learning for training diverse deep ensembles"

Connected through hyperedge HE-4 (induced by source paper "multi modal geometric learning for grasping and manipulation"):
  with members:
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "3d shape perception from monocular vision touch and shape priors"
    - "shape completion enabled robotic grasping"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "geodabs trajectory indexing meets fingerprinting at scale"):
  with members:
    - "roaring bitmaps implementation of an optimized software library"

Connected through hyperedge HE-6 (induced by source paper "sampling as a baseline optimizer for search based software engineering"):
  with members:
    - "beyond evolutionary algorithms for search based software engineering"
    - "riot a novel stochastic method for rapidly configuring cloud based workflows"
    - "easy over hard a case study on deep learning"
    - "tuning for software analytics"

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.DM(Discrete Mathematics)