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: "conspiracies between learning algorithms circuit lower bounds and pseudorandomness"
  Abstract: We prove several results giving new and stronger connections between learning, circuit lower bounds and pseudorandomness. Among other results, we show a generic learning speedup lemma, equivalences between various learning models in the exponential time and subexponential time regimes, a dichotomy between learning and pseudorandomness, consequences of non-trivial learning for circuit lower bounds, Karp-Lipton theorems for probabilistic exponential time, and NC$^1$-hardness for the Minimum Circuit Size Problem.

Connected through hyperedge HE-1 (induced by source paper "algorithms and lower bounds for de morgan formulas of low communication leaf gates"):
  with members:
    - "improved pseudorandom generators from pseudorandom multi switching lemmas"
    - "tighter connections between formula sat and shaving logs"
    - "classical algorithms from quantum and arthur merlin communication protocols"
    - "fooling polytopes"
    - "on polynomial approximations to ac 0"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "fooling functions of halfspaces under product distributions"

Connected through hyperedge HE-2 (induced by source paper "the power of comparisons for actively learning linear classifiers"):
  with members:
    - "generalized comparison trees for point location problems"
    - "near optimal linear decision trees for k sum and related problems"
    - "active classification with comparison queries"
    - "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 "beyond natural proofs hardness magnification and locality"):
  with members:
    - "addition is exponentially harder than counting for shallow monotone circuits"

Connected through hyperedge HE-4 (induced by source paper "pseudodeterministic constructions in subexponential time"):
  with members:
    - "pseudorandomness and combinatorial constructions"

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)