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: "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"
  Abstract: In order to formally understand the power of neural computing, we first need to crack the frontier of threshold circuits with two and three layers, a regime that has been surprisingly intractable to analyze. We prove the first super-linear gate lower bounds and the first super-quadratic wire lower bounds for depth-two linear threshold circuits with arbitrary weights, and depth-three majority circuits computing an explicit function. #R##N#$\bullet$ We prove that for all $\epsilon\gg \sqrt{\log(n)/n}$, the linear-time computable Andreev's function cannot be computed on a $(1/2+\epsilon)$-fraction of $n$-bit inputs by depth-two linear threshold circuits of $o(\epsilon^3 n^{3/2}/\log^3 n)$ gates, nor can it be computed with $o(\epsilon^{3} n^{5/2}/\log^{7/2} n)$ wires. This establishes an average-case ``size hierarchy'' for threshold circuits, as Andreev's function is computable by uniform depth-two circuits of $o(n^3)$ linear threshold gates, and by uniform depth-three circuits of $O(n)$ majority gates. #R##N#$\bullet$ We present a new function in $P$ based on small-biased sets, which we prove cannot be computed by a majority vote of depth-two linear threshold circuits with $o(n^{3/2}/\log^3 n)$ gates, nor with $o(n^{5/2}/\log^{7/2}n)$ wires. #R##N#$\bullet$ We give tight average-case (gate and wire) complexity results for computing PARITY with depth-two threshold circuits; the answer turns out to be the same as for depth-two majority circuits. #R##N#The key is a new random restriction lemma for linear threshold functions. Our main analytical tool is the Littlewood-Offord Lemma from additive combinatorics.

Connected through hyperedge HE-1 (induced by source paper "a deep conditioning treatment of neural networks"):
  with members:
    - "backward feature correction how deep learning performs deep learning"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "two models of double descent for weak features"
    - "size free generalization bounds for convolutional neural networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "implicit regularization in deep matrix factorization"
    - "on the learnability of deep random networks"

Connected through hyperedge HE-2 (induced by source paper "better depth width trade offs for neural networks through the lens of dynamical systems"):
  with members:
    - "on the expressive power of deep polynomial neural networks"
    - "is deeper better only when shallow is good"
    - "benefits of depth in neural networks"
    - "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-3 (induced by source paper "approximate is good enough probabilistic variants of dimensional and margin complexity"):
  with members:
    - "backward feature correction how deep learning performs deep learning"
    - "on the power and limitations of random features for understanding neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "sgd learns the conjugate kernel class of the network"
    - "probabilistic rank and matrix rigidity"

Connected through hyperedge HE-4 (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-5 (induced by source paper "degree d chow parameters robustly determine degree d ptfs and algorithmic applications"):
  with members:
    - "learning geometric concepts with nasty noise"
    - "hardness of learning noisy halfspaces using polynomial thresholds"
    - "robust estimators in high dimensions without the computational intractability"
    - "pseudorandomness via the discrete fourier transform"
    - "complexity theoretic limitations on learning halfspaces"
    - "a ptas for agnostically learning halfspaces"
    - "analysis of boolean functions"
    - "deterministic approximate counting for juntas of degree 2 polynomial threshold functions"

Connected through hyperedge HE-6 (induced by source paper "toward super polynomial size lower bounds for depth two threshold circuits"):
  with members:
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "tighter connections between formula sat and shaving logs"
    - "limits on representing boolean functions by linear combinations of simple functions thresholds relus and low degree polynomials"
    - "fine grained complexity meets ip pspace"
    - "weights at the bottom matter when the top is heavy"
    - "probabilistic rank and matrix rigidity"
    - "simulating branching programs with edit distance and friends or a polylog shaved is a lower bound made"
    - "new algorithms and lower bounds for circuits with linear threshold gates"

Connected through hyperedge HE-7 (induced by source paper "limits on representing boolean functions by linear combinations of simple functions thresholds relus and low degree polynomials"):
  with members:
    - "weights at the bottom matter when the top is heavy"
    - "depth separation for neural networks"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "understanding deep neural networks with rectified linear units"
    - "depth width tradeoffs in approximating natural functions with neural networks"
    - "the power of depth for feedforward neural networks"
    - "new algorithms and lower bounds for circuits with linear threshold gates"

Connected through hyperedge HE-8 (induced by source paper "quantified derandomization of linear threshold circuits"):
  with members:
    - "deterministic search for cnf satisfying assignments in almost polynomial time"
    - "average case lower bounds and satisfiability algorithms for small threshold circuits"
    - "randomness extraction in ac0 and with small locality"
    - "pseudorandomness via the discrete fourier transform"
    - "analysis of boolean functions"
    - "new algorithms and lower bounds for circuits with linear threshold gates"
    - "almost optimal pseudorandom generators for spherical caps"
    - "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)