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: "approximating the volume of unions and intersections of high dimensional geometric objects"
  Abstract: We consider the computation of the volume of the union of high-dimensional geometric objects. While showing that this problem is #P-hard already for very simple bodies, we give a fast FPRAS for all objects where one can (1) test whether a given point lies inside the object, (2) sample a point uniformly, and (3) calculate the volume of the object in polynomial time. It suffices to be able to answer all three questions approximately. We show that this holds for a large class of objects. It implies that Klee's measure problem can be approximated efficiently even though it is #P-hard and hence cannot be solved exactly in polynomial time in the number of dimensions unless P=NP. Our algorithm also allows to efficiently approximate the volume of the union of convex bodies given by weak membership oracles. For the analogous problem of the intersection of high-dimensional geometric objects we prove #P-hardness for boxes and show that there is no multiplicative polynomial-time 2^d^^^1^^^-^^^@e-approximation for certain boxes unless NP=BPP, but give a simple additive polynomial-time @e-approximation.

Connected through hyperedge HE-1 (induced by source paper "on the approximability of weighted model integration on dnf structures"):
  with members:
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "on hashing based approaches to approximate dnf counting"
    - "geodesic walks in polytopes"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "distribution aware sampling and weighted model counting for sat"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "probabilistic theorem proving"

Connected through hyperedge HE-2 (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"
    - "computing the approximate convex hull in high dimensions"
    - "deep learning in neural networks"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "learning choice functions"):
  with members:
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "self normalizing neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "approximating the least hypervolume contributor np hard in general but fast in practice"
    - "learning to order things"

Connected through hyperedge HE-4 (induced by source paper "approximate set union via approximate randomization"):
  with members:
    - "optimal streaming and tracking distinct elements with high probability"
    - "partial sublinear time approximation and inapproximation for maximum coverage"
    - "tracking the frequency moments at all times"

Connected through hyperedge HE-5 (induced by source paper "real time seamless single shot 6d object pose prediction"):
  with members:
    - "3d pose regression using convolutional neural networks"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "global hypothesis generation for 6d object pose estimation"

Connected through hyperedge HE-6 (induced by source paper "temporal and physical reasoning for perception based robotic manipulation"):
  with members:
    - "semantic scene completion from a single depth image"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "costar instructing collaborative robots with behavior trees and vision"

Connected through hyperedge HE-7 (induced by source paper "maximum volume subset selection for anchored boxes"):
  with members:
    - "bringing order to special cases of klee s measure problem"
    - "approximation schemes for maximum weight independent set of rectangles"
    - "approximating the least hypervolume contributor np hard in general but fast in practice"

Connected through hyperedge HE-8 (induced by source paper "a box decomposition algorithm to compute the hypervolume indicator"):
  with members:
    - "on the representation of the search region in multi objective optimization"

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)