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: "regression depth and center points"
  Abstract: We show that, for any set of n points in d dimensions, there exists a hyperplane with regression depth at least \(\lceil n/(d+1)\rceil\) , as had been conjectured by Rousseeuw and Hubert. Dually, for any arrangement of n hyperplanes in d dimensions there exists a point that cannot escape to infinity without crossing at least \(\lceil n/(d+1)\rceil\) hyperplanes. We also apply our approach to related questions on the existence of partitions of the data into subsets such that a common plane has nonzero regression depth in each subset, and to the computational complexity of regression depth problems.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial nets for robust scatter estimation a proper scoring rule perspective"):
  with members:
    - "deconstructing generative adversarial networks"
    - "approximability of discriminators implies diversity in gans"
    - "on the discrimination generalization tradeoff in gans"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "improved techniques for training gans"
    - "least squares generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "network essence pagerank completion and centrality conforming markov chains"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "network composition from multi layer data"
    - "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"
    - "approximate undirected maximum flows in o m polylog n time"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "graphical models for game theory"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "efficient computation of the shapley value for game theoretic network centrality"

Connected through hyperedge HE-3 (induced by source paper "relative p epsilon approximations in geometry"):
  with members:
    - "semi algebraic range reporting and emptiness searching with applications"

Connected through hyperedge HE-4 (induced by source paper "deterministic sampling and range counting in geometric data streams"):
  with members:
    - "multivariate regression depth"

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.CG(Computational Geometry)