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: "computing the approximate convex hull in high dimensions"
  Abstract: In this paper, an effective method with time complexity of $\mathcal{O}(K^{3/2}N^2\log \frac{K}{\epsilon_0})$ is introduced to find an approximation of the convex hull for $N$ points in dimension $n$, where $K$ is close to the number of vertices of the approximation. Since the time complexity is independent of dimension, this method is highly suitable for the data in high dimensions. Utilizing a greedy approach, the proposed method attempts to find the best approximate convex hull for a given number of vertices. The approximate convex hull can be a helpful substitute for the exact convex hull for on-line processes and applications that have a favorable trade off between accuracy and parsimony.

Connected through hyperedge HE-1 (induced by source paper "on geometric structure of activation spaces in neural networks"):
  with members:
    - "on generalization error bounds of noisy gradient methods for non convex learning"
    - "communitygan community detection with generative adversarial nets"
    - "acekg a large scale knowledge graph for academic data mining"
    - "towards understanding learning representations to what extent do different neural networks learn the same representation"
    - "curvature based comparison of two neural networks"
    - "deep learning in neural networks"
    - "deepwalk online learning of social representations"
    - "approximating the volume of unions and intersections of high dimensional geometric objects"

Connected through hyperedge HE-2 (induced by source paper "semantic explanations of predictions"):
  with members:
    - "context aware captions from context agnostic supervision"
    - "improved techniques for training gans"
    - "rationalizing neural predictions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "visualizing and understanding neural models in nlp"

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)