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: "classical hardness of learning with errors"
  Abstract: We show that the Learning with Errors (LWE) problem is classically at least as hard as standard worst-case lattice problems, even with polynomial modulus. Previously this was only known under quantum reductions. #R##N#Our techniques capture the tradeoff between the dimension and the modulus of LWE instances, leading to a much better understanding of the landscape of the problem. The proof is inspired by techniques from several recent cryptographic constructions, most notably fully homomorphic encryption schemes.

Connected through hyperedge HE-1 (induced by source paper "faster provable sieving algorithms for the shortest vector problem and the closest vector problem on lattices in 120001 p norm"):
  with members:
    - "the projection games conjecture and the hardness of approximation of ssat and related problems"
    - "improved algorithms for the shortest vector problem and the closest vector problem in the infinity norm"
    - "gap s eth hardness of svp"
    - "just take the average an embarrassingly simple 2 n time algorithm for svp and cvp"
    - "on the quantitative hardness of cvp"
    - "solving the shortest vector problem in 2 n time via discrete gaussian sampling"
    - "near optimal deterministic algorithms for volume computation via m ellipsoids"
    - "tensor based hardness of the shortest vector problem to within almost polynomial factors"

Connected through hyperedge HE-2 (induced by source paper "the projection games conjecture and the hardness of approximation of ssat and related problems"):
  with members:
    - "gap s eth hardness of svp"
    - "on the quantitative hardness of cvp"
    - "tensor based hardness of the shortest vector problem to within almost polynomial factors"
    - "covering cubes and the closest vector problem"

Connected through hyperedge HE-3 (induced by source paper "sapphire a configurable crypto processor for post quantum lattice based protocols"):
  with members:
    - "2 3 an energy efficient configurable lattice cryptography processor for the quantum secure internet of things"
    - "an energy efficient reconfigurable dtls cryptographic engine for end to end security in iot applications"
    - "quantum computation and lattice problems"

Connected through hyperedge HE-4 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling convergence analysis and enhancement"):
  with members:
    - "boosted kz and lll algorithms"
    - "lattice gaussian sampling by markov chain monte carlo bounded distance decoding and trapdoor sampling"
    - "sensitivity gains by mismatched probabilistic shaping for optical communication systems"
    - "polar codes and polar lattices for independent fading channels"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "achieving secrecy capacity of the gaussian wiretap channel with polar lattices"
    - "fifty years of mimo detection the road to large scale mimos"
    - "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"

Connected through hyperedge HE-5 (induced by source paper "ppp completeness with connections to cryptography"):
  with members:
    - "on the polynomial parity argument complexity of the combinatorial nullstellensatz"
    - "consensus halving is ppa complete"
    - "cls new problems and completeness"
    - "a converse to banach s fixed point theorem and its cls completeness"
    - "local max cut in smoothed polynomial time"
    - "integer factoring and modular square roots"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "number balancing is as hard as minkowski s theorem and shortest vector"

Connected through hyperedge HE-6 (induced by source paper "lattice gaussian sampling by markov chain monte carlo convergence rate and decoding complexity"):
  with members:
    - "independent metropolis hastings klein algorithm for lattice gaussian sampling"
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"
    - "markov chain monte carlo algorithms for lattice gaussian sampling"
    - "massive mimo for next generation wireless systems"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "on the closest vector problem with a distance guarantee"
    - "decoding by embedding correct decoding radius and dmt optimality"

Connected through hyperedge HE-7 (induced by source paper "learning with errors and extrapolated dihedral cosets"):
  with members:
    - "quantum computation and lattice problems"
    - "new lattice based cryptographic constructions"

Connected through hyperedge HE-8 (induced by source paper "solving the closest vector problem in 2 n time the discrete gaussian strikes again"):
  with members:
    - "discrete gaussian sampling reduces to cvp and svp"
    - "solving the shortest vector problem in 2 n time via discrete gaussian sampling"
    - "short paths on the voronoi graph and the closest vector problem with preprocessing"
    - "on the closest vector problem with a distance guarantee"
    - "tensor based hardness of the shortest vector problem to within almost polynomial factors"
    - "lattice sparsification and the approximate closest vector problem"
    - "approximating the closest vector problem using an approximate shortest vector oracle"
    - "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)