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: "weights at the bottom matter when the top is heavy"
  Abstract: Proving super-polynomial lower bounds against depth-2 threshold circuits of the form THR of THR is a well-known open problem that represents a frontier of our understanding in boolean circuit complexity. By contrast, exponential lower bounds on the size of THR of MAJ circuits were shown by Razborov and Sherstov (SIAM J.Comput., 2010) even for computing functions in depth-3 AC^0. Yet, no separation among the two depth-2 threshold circuit classes were known. In fact, it is not clear a priori that they ought to be different. In particular, Goldmann, Hastad and Razborov (Computational Complexity, 1992) showed that the class MAJ of MAJ is identical to the class MAJ of THR. #R##N#In this work, we provide an exponential separation between THR of MAJ and THR of THR. We achieve this by showing a function f that is computed by linear size THR of THR circuits and yet has exponentially large sign rank. This, by a well-known result, implies that f requires exponentially large THR of MAJ circuits to be computed. Our result suggests that the sign rank method alone is unlikely to prove strong lower bounds against THR of THR circuits. #R##N#The main technical ingredient of our work is to prove a strong sign rank lower bound for an XOR function. This requires novel use of approximation theoretic tools.

Connected through hyperedge HE-1 (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"
    - "probabilistic rank and matrix rigidity"
    - "simulating branching programs with edit distance and friends or a polylog shaved is a lower bound made"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"
    - "new algorithms and lower bounds for circuits with linear threshold gates"

Connected through hyperedge HE-2 (induced by source paper "limits on representing boolean functions by linear combinations of simple functions thresholds relus and low degree polynomials"):
  with members:
    - "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"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"
    - "new algorithms and lower bounds for circuits with linear threshold gates"

Connected through hyperedge HE-3 (induced by source paper "lower bounds over boolean inputs for deep neural networks with relu gates"):
  with members:
    - "depth separation for neural networks"
    - "theory ii landscape of the empirical risk in deep learning"
    - "error bounds for approximations with deep relu networks"
    - "benefits of depth in neural networks"
    - "why deep neural networks for function approximation"
    - "depth width tradeoffs in approximating natural functions with neural networks"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "the power of depth for feedforward neural networks"

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)