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 "barriers for rank methods in arithmetic complexity"):
  Abstract: Arithmetic complexity is considered simpler to understand than Boolean complexity, namely computing Boolean functions via logical gates. And indeed, we seem to have significantly more lower bound techniques and results in arithmetic complexity than in Boolean complexity. Despite many successes and rapid progress, however, challenges like proving super-polynomial lower bounds on circuit or formula size for explicit polynomials, or super-linear lower bounds on explicit 3-dimensional tensors, remain elusive. #R##N#At the same time, we have plenty more "barrier results" for failing to prove basic lower bounds in Boolean complexity than in arithmetic complexity. Finding barriers to arithmetic lower bound techniques seem harder, and despite some attempts we have no excuses of similar quality for these failures in arithmetic complexity. This paper aims to add to this study. #R##N#We address rank methods, which were long recognized as encompassing and abstracting almost all known arithmetic lower bounds to-date, including the most recent impressive successes. Rank methods (or flattenings) are also in wide use in algebraic geometry for proving tensor rank and symmetric tensor rank lower bounds. Our main results are barriers to these methods. In particular, #R##N#1. Rank methods cannot prove better than $\Omega_d (n^{\lfloor d/2 \rfloor})$ lower bound on the tensor rank of any $d$-dimensional tensor of side $n$. (In particular, they cannot prove super-linear, indeed even $>8n$ tensor rank lower bounds for any 3-dimensional tensors.) #R##N#2. Rank methods cannot prove $\Omega_d (n^{\lfloor d/2 \rfloor})$ on the Waring rank of any $n$-variate polynomial of degree $d$. (In particular, they cannot prove such lower bounds on stronger models, including depth-3 circuits.)

Includes members:
  Member "succinct hitting sets and barriers to proving algebraic circuits lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "implementing geometric complexity theory on the separation of orbit closures via symmetries")
  Member "on the optimization landscape of tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data")
  Member "explicit polynomial sequences with maximal spaces of partial derivatives and a question of k mulmuley":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "more barriers for rank methods via a numeric to symbolic transfer")
  Member "towards an algebraic natural proofs barrier via polynomial identity testing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "implementing geometric complexity theory on the separation of orbit closures via symmetries")
  Member "a note on amortized branching program complexity":
    also connected through other hyperedges:
      (none)
  Member "an exponential lower bound for homogeneous depth 5 circuits over finite fields":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards an algebraic natural proofs barrier via polynomial identity testing")
  Member "unifying known lower bounds via geometric complexity theory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "succinct hitting sets and barriers to proving algebraic circuits lower bounds")
  Member "learning mixtures of spherical gaussians moment methods and spectral decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians")

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)