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: "a deterministic polynomial space construction for eps nets under any norm"
  Abstract: We give a deterministic polynomial space construction for nearly optimal eps-nets with respect to any input n-dimensional convex body K and norm |.|. More precisely, our algorithm can build and iterate over an eps-net of K with respect to |.| in time 2^O(n) x (size of the optimal net) using only poly(n)-space. This improves on previous constructions of Alon et al [STOC 2013] which achieve either a 2^O(n) approximation or an n^O(n) approximation of the optimal net size using 2^n space and poly(n)-space respectively. As in their work, our algorithm relies on the mathematically classical approach of building thin lattice coverings of space, which reduces the task of constructing eps-nets to the problem of enumerating lattice points. Our main technical contribution is a deterministic 2^O(n)-time and poly(n)-space construction of thin lattice coverings of space with respect to any convex body, where enumeration in these lattices can be efficiently performed using poly(n)-space. This also yields the first existential construction of poly(n)-space enumerable thin covering lattices for general convex bodies, which we believe is of independent interest. Our construction combines the use of the M-ellipsoid from convex geometry with lattice sparsification and densification techniques. #R##N#As an application, we give a 2^O(n)(1+1/eps)^n time and poly(n)-space deterministic algorithm for computing a (1+eps)^n approximation to the volume of a general convex body, which nearly matches the lower bounds for volume estimation in the oracle model (the dependence on eps is larger by a factor 2 in the exponent). This improves on the previous results of Dadush and Vempala [PNAS 2013], which gave the above result only for symmetric bodies and achieved a dependence on eps of (1+log^{5/2}(1/eps)/eps^2)^n.

Connected through hyperedge HE-1 (induced by source paper "covering convex bodies and the closest vector problem"):
  with members:
    - "on compact representations of voronoi cells of lattices"
    - "just take the average an embarrassingly simple 2 n time algorithm for svp and cvp"
    - "lattice sparsification and the approximate closest vector problem"
    - "a o 1 eps 2 n time sieving algorithm for approximate integer programming"
    - "covering cubes and the closest vector problem"
    - "enumerative lattice algorithms in any norm via m ellipsoid coverings"

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.CC(Computational Complexity)