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: "maximally consistent sampling and the jaccard index of probability distributions"
  Abstract: We introduce simple, efficient algorithms for computing a MinHash of a probability distribution, suitable for both sparse and dense data, with equivalent running times to the state of the art for both cases. The collision probability of these algorithms is a new measure of the similarity of positive vectors which we investigate in detail. We describe the sense in which this collision probability is optimal for any Locality Sensitive Hash based on sampling. We argue that this similarity measure is more useful for probability distributions than the similarity pursued by other algorithms for weighted MinHash, and is the natural generalization of the Jaccard index.

Connected through hyperedge HE-1 (induced by source paper "inferring spatial uncertainty in object detection"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "pixor real time 3d object detection from point clouds"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "learning an uncertainty aware object detector for autonomous driving"
    - "iou loss for 2d 3d object detection"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "bayesod a bayesian approach for uncertainty estimation in deep object detectors"
    - "distance iou loss faster and better learning for bounding box regression"

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"
    - "a note on the triangle inequality for the jaccard distance"
    - "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 "probminhash a class of locality sensitive hash algorithms for the probability jaccard similarity"):
  with members:
    - "fast random integer generation in an interval"
    - "bagminhash minwise hashing algorithm for weighted sets"
    - "fast similarity sketching"
    - "optimal densification for fast and accurate minwise hashing"
    - "superminhash a new minwise hashing algorithm for jaccard similarity estimation"
    - "b bit minwise hashing"

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)