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: "an average case depth hierarchy theorem for boolean circuits"
  Abstract: We prove an average-case depth hierarchy theorem for Boolean circuits over the standard basis of $\mathsf{AND}$, $\mathsf{OR}$, and $\mathsf{NOT}$ gates. Our hierarchy theorem says that for every $d \geq 2$, there is an explicit $n$-variable Boolean function $f$, computed by a linear-size depth-$d$ formula, which is such that any depth-$(d-1)$ circuit that agrees with $f$ on $(1/2 + o_n(1))$ fraction of all inputs must have size $\exp({n^{\Omega(1/d)}}).$ This answers an open question posed by H{\aa}stad in his Ph.D. thesis. #R##N#Our average-case depth hierarchy theorem implies that the polynomial hierarchy is infinite relative to a random oracle with probability 1, confirming a conjecture of H{\aa}stad, Cai, and Babai. We also use our result to show that there is no "approximate converse" to the results of Linial, Mansour, Nisan and Boppana on the total influence of small-depth circuits, thus answering a question posed by O'Donnell, Kalai, and Hatami. #R##N#A key ingredient in our proof is a notion of \emph{random projections} which generalize random restrictions.

Connected through hyperedge HE-1 (induced by source paper "depth width trade offs for relu networks via sharkovsky s theorem"):
  with members:
    - "the route to chaos in routing games population increase drives period doubling instability chaos inefficiency with price of anarchy equal to one"
    - "on the expressive power of deep polynomial neural networks"
    - "is deeper better only when shallow is good"
    - "benefits of depth in neural networks"
    - "why deep neural networks for function approximation"
    - "understanding deep neural networks with rectified linear units"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"

Connected through hyperedge HE-2 (induced by source paper "improved pseudorandom generators from pseudorandom multi switching lemmas"):
  with members:
    - "on polynomial approximations to ac 0"
    - "near optimal small depth lower bounds for small distance connectivity"
    - "the average sensitivity of bounded depth formulas"
    - "pseudorandomness for regular branching programs via fourier analysis"
    - "a counterexample to the generalized linial nisan conjecture"

Connected through hyperedge HE-3 (induced by source paper "a near optimal depth hierarchy theorem for small depth multilinear circuits"):
  with members:
    - "small depth multilinear formula lower bounds for iterated matrix multiplication with applications"
    - "near optimal small depth lower bounds for small distance connectivity"
    - "on the power of homogeneous depth 4 arithmetic circuits"
    - "improved bounds for reduction to depth 4 and depth 3"
    - "arithmetic circuits the chasm at depth four gets wider"

Connected through hyperedge HE-4 (induced by source paper "understanding deep neural networks with rectified linear units"):
  with members:
    - "bounding and counting linear regions of deep neural networks"
    - "error bounds for approximations with deep relu networks"
    - "reliably learning the relu in polynomial time"
    - "distribution specific hardness of learning neural networks"
    - "benefits of depth in neural networks"
    - "why deep neural networks for function approximation"
    - "depth width tradeoffs in approximating natural functions with neural networks"
    - "the power of depth for feedforward neural networks"

Connected through hyperedge HE-5 (induced by source paper "benefits of depth in neural networks"):
  with members:
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"
    - "on the expressive efficiency of sum product networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-6 (induced by source paper "on the expressive power of deep learning a tensor analysis"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "the power of depth for feedforward neural networks"
    - "deep convolutional networks are hierarchical kernel machines"
    - "representation benefits of deep feedforward networks"
    - "statistical machine translation features with multitask tensor networks"
    - "very deep convolutional networks for large scale image recognition"
    - "on the expressive efficiency of sum product networks"
    - "speeding up convolutional neural networks using fine tuned cp decomposition"

Connected through hyperedge HE-7 (induced by source paper "the power of depth for feedforward neural networks"):
  with members:
    - "representation benefits of deep feedforward networks"
    - "on the expressive power of deep learning a tensor analysis"
    - "on the expressive efficiency of sum product networks"

Connected through hyperedge HE-8 (induced by source paper "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"):
  with members:
    - "average case lower bounds and satisfiability algorithms for small threshold circuits"
    - "new algorithms and lower bounds for circuits with linear threshold gates"
    - "a satisfiability algorithm for sparse depth two threshold circuits"

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)