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: "generalized comparison trees for point location problems"
  Abstract: Let $H$ be an arbitrary family of hyper-planes in $d$-dimensions. We show that the point-location problem for $H$ can be solved by a linear decision tree that only uses a special type of queries called \emph{generalized comparison queries}. These queries correspond to hyperplanes that can be written as a linear combination of two hyperplanes from $H$; in particular, if all hyperplanes in $H$ are $k$-sparse then generalized comparisons are $2k$-sparse. The depth of the obtained linear decision tree is polynomial in $d$ and logarithmic in $|H|$, which is comparable to previous results in the literature that use general linear queries. #R##N#This extends the study of comparison trees from a previous work by the authors [Kane {et al.}, FOCS 2017]. The main benefit is that using generalized comparison queries allows to overcome limitations that apply for the more restricted type of comparison queries. #R##N#Our analysis combines a seminal result of Forster regarding sets in isotropic position [Forster, JCSS 2002], the margin-based inference dimension analysis for comparison queries from [Kane {et al.}, FOCS 2017], and compactness arguments.

Connected through hyperedge HE-1 (induced by source paper "noise tolerant reliable active classification with comparison queries"):
  with members:
    - "the power of comparisons for actively learning linear classifiers"
    - "sorting by swaps with noisy comparisons"
    - "optimal sorting with persistent comparison errors"
    - "active classification with comparison queries"
    - "efficient learning of linear separators under bounded noise"
    - "minimax analysis of active learning"
    - "active and passive learning of linear separators under log concave distributions"
    - "sorting from noisy information"

Connected through hyperedge HE-2 (induced by source paper "the power of comparisons for actively learning linear classifiers"):
  with members:
    - "near optimal linear decision trees for k sum and related problems"
    - "active classification with comparison queries"
    - "conspiracies between learning algorithms circuit lower bounds and pseudorandomness"
    - "efficient learning of linear separators under bounded noise"
    - "active and passive learning of linear separators under log concave distributions"
    - "adaptively learning the crowd kernel"

Connected through hyperedge HE-3 (induced by source paper "near optimal linear decision trees for k sum and related problems"):
  with members:
    - "active classification with comparison queries"
    - "the decision tree complexity for k sum is at most nearly quadratic"
    - "improved bounds for 3sum k sum and linear degeneracy"
    - "clustered integer 3sum via additive combinatorics"
    - "solving k sum using few linear queries"
    - "threesomes degenerates and love triangles"
    - "higher lower bounds from the 3sum conjecture"
    - "faster all pairs shortest paths via circuit complexity"

Connected through hyperedge HE-4 (induced by source paper "decomposing arrangements of hyperplanes vc dimension combinatorial dimension and point location"):
  with members:
    - "near optimal linear decision trees for k sum and related problems"
    - "active classification with comparison queries"
    - "shortest path in a polygon using sublinear space"

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)