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 projection games conjecture and the hardness of approximation of ssat and related problems"
  Abstract: The Super-SAT or SSAT problem was introduced by Dinur, Kindler, Raz and Safra[2002,2003] to prove the NP-hardness of approximation of two popular lattice problems - Shortest Vector Problem (SVP) and Closest Vector Problem (CVP). They conjectured that SSAT is NP-hard to approximate to within factor $n^c$ for some constant $c>0$, where $n$ is the size of the SSAT instance. In this paper we prove this conjecture assuming the Projection Games Conjecture (PGC), given by Moshkovitz[2012]. This implies hardness of approximation of SVP and CVP within polynomial factors, assuming the Projection Games Conjecture. #R##N#We also reduce SSAT to the Nearest Codeword Problem (NCP) and Learning Halfspace Problem (LHP), as considered by Arora, Babai, Stern and Sweedyk[1997]. This proves that both these problems are NP-hard to approximate within factor $N^{c'/\log\log n}$ for some constant $c'>0$ where $N$ is the size of the instances of the respective problems. Assuming the Projection Games Conjecture these problems are proved to be NP-hard to approximate within polynomial factors.

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:
    - "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"
    - "classical hardness of learning with errors"
    - "tensor based hardness of the shortest vector problem to within almost polynomial factors"

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)