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: "the curse of medium dimension for geometric problems in almost every norm"
  Abstract: Given a point-set, finding the Closest Pair of points in the set, determining its Diameter, and computing a Euclidean Minimum Spanning Tree are amongst the most fundamental problems in Computer Science and Computational Geometry. In this paper, we study the complexity of these three problems in medium dimension, i.e., dimension $d=\Theta(\log N)$, and for various $\ell_p$-norms. We show a reduction from SAT to these geometric problems in medium dimension, thus showing that these problems have no subquadratic-time algorithm, i.e., no $O(N^{2-\varepsilon}2^{o(d)})$-time algorithm for any $\varepsilon>0$, under the Strong Exponential-Time Hypothesis (SETH). In particular, under SETH we prove the following results: #R##N#$\bullet$ For every $p\geq 0$, there is no subquadratic-time algorithm for the Diameter problem, in the $\ell_p$-norm and dimension $d=\Theta(\log N)$. #R##N#$\bullet$ There is no subquadratic-time algorithm for the Euclidean Minimum Spanning Tree problem in dimension $d=\Theta(\log N)$. #R##N#$\bullet$ For every $p\in \mathbb{R}_{>2}\cup \{\infty\}$, there is no subquadratic-time algorithm for the Closest Pair problem in the $\ell_p$-norm and dimension $d=\Theta(\log N)$. Additionally, we prove a subquadratic lower bound for a generalization of the Closest Pair problem, namely the Set Closest Pair problem for all norms $\ell_p$, $p\in\mathbb{R}_{\geq 1}\cup\{\infty\}$. #R##N#Many of our proofs go through a reduction from the Bichromatic Closest Pair problem; we prove that for every $p\in\mathbb{R}^+\cup \{\infty\}$, there is no subquadratic-time algorithm for the Bichromatic Closest Pair problem in the $\ell_p$-norm and dimension $d=\Theta(\log N)$, extending a result by Alman and Williams [FOCS 2015]. To show certain limitations of our techniques, we also prove a Point-Set Separation lemma for the $\ell_2$-norm, which might be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "hardness of approximate nearest neighbor search"):
  with members:
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "distributed pcp theorems for hardness of approximation in p"
    - "optimal las vegas locality sensitive data structures"
    - "randomized approximate nearest neighbor search with limited adaptivity"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-2 (induced by source paper "distributed pcp theorems for hardness of approximation in p"):
  with members:
    - "fully dynamic maximal matching in constant update time"
    - "new deterministic approximation algorithms for fully dynamic matching"
    - "on the hardness of partially dynamic graph problems and connections to diameter"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "on the complexity of inner product similarity join"
    - "design of dynamic algorithms via primal dual method"
    - "on problems as hard as cnf sat"

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)