human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "detecting patterns can be hard circuit lower bounds for the string matching problem"):
  Abstract: Detecting patterns in strings and images is a fundamental and well studied problem. We study the circuit complexity of the string matching problem under two popular choices of gates: De Morgan and threshold gates. For strings of length $n$ and patterns of length $\log n \ll k \leq n -o(n)$, we prove super polynomial lower bounds for De Morgan circuits of depth 2, and nearly linear lower bounds for depth 2 threshold circuits. For unbounded depth and $k \geq 2$, we prove a linear lower bound for (unbounded fan-in) De Morgan circuits. For certain values of $k$, we prove a $\Omega(\sqrt{n}/\log n)$ lower bound for general (no depth restriction) threshold circuits. Our proof for threshold circuits builds on a curious connection between detecting patterns and evaluating Boolean functions when the truth table of the function is given explicitly. Finally, we provide upper bounds on the size of circuits that solve the string matching problem.

Includes members:
  Member "deleting and testing forbidden patterns in multi dimensional arrays":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "testing hereditary properties of ordered graphs and matrices")
  Member "the power of depth for feedforward neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "the optimal sample complexity of pac learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond dropout feature map distortion to regularize deep neural networks")
  Member "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "exact solutions to the nonlinear dynamics of learning in deep linear neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "a discrepancy lower bound for information complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "quantum communication query tradeoffs")

Question: Please tell me which class the source paper 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)