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: "greedy strategy works for k center clustering with outliers and coreset construction"
  Abstract: We study the problem of $k$-center clustering with outliers in arbitrary metrics and Euclidean space. Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithm with low complexity for this problem. Our idea is inspired by the greedy method, Gonzalez's algorithm, for solving the problem of ordinary $k$-center clustering. Based on some novel observations, we show that this greedy strategy actually can handle $k$-center clustering with outliers efficiently, in terms of clustering quality and time complexity. We further show that the greedy approach yields small coreset for the problem in doubling metrics, so as to reduce the time complexity significantly. Our algorithms are easy to implement in practice. We test our method on both synthetic and real datasets. The experimental results suggest that our algorithms can achieve near optimal solutions and yield lower running times comparing with existing methods.

Connected through hyperedge HE-1 (induced by source paper "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "the non uniform k center problem"
    - "adversarial machine learning at scale"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "recovering the optimal solution by dual random projection"
    - "random projections for linear support vector machines"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-2 (induced by source paper "on metric dbscan with low doubling dimension"):
  with members:
    - "dbscan towards fast and scalable density clustering"
    - "the non uniform k center problem"

Connected through hyperedge HE-3 (induced by source paper "a composable coreset for k center in doubling metrics"):
  with members:
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "massively parallel algorithms and hardness for single linkage clustering under ell_p distances"
    - "efficient parallel algorithms for k center clustering"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "randomized composable core sets for distributed submodular maximization"
    - "approximating k median via pseudo approximation"
    - "fast clustering using mapreduce"
    - "fast construction of nets in low dimensional metrics and their applications"

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)