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 "sum of squares meets nash optimal lower bounds for finding any equilibrium"):
  Abstract: Several works have shown unconditional hardness (via integrality gaps) of computing equilibria using strong hierarchies of convex relaxations. Such results however only apply to the problem of computing equilibria that optimize a certain objective function and not to the (arguably more fundamental) task of finding \emph{any} equilibrium. #R##N#We present an algorithmic model based on the sum-of-squares (SoS) hierarchy that allows escaping this inherent limitation of integrality gaps. In this model, algorithms access the input game only through a relaxed solution to the natural SoS relaxation for computing equilibria. They can then adaptively construct a list of candidate solutions and invoke a verification oracle to check if any candidate on the list is a solution. This model captures most well-studied approximation algorithms such as those for Max-Cut, Sparsest Cut, and Unique-Games. #R##N#The state-of-the-art algorithms for computing exact and approximate equilibria in two-player, n-strategy games are captured in this model and require that at least one of i) size (~ running time) of the SoS relaxation or ii) the size of the list of candidates, be at least $2^{\Omega(n)}$ and $n^{\Omega(\log{(n)})}$ respectively. Our main result shows a lower bound that matches these upper bound up to constant factors in the exponent. #R##N#This can be interpreted as an unconditional confirmation, in our restricted algorithmic framework, of Rubinstein's recent conditional hardness \cite{Rub} for computing approximate equilibria. #R##N#Our proof strategy involves constructing a family of games that all share a common sum-of-squares solution but every (approximate) equilibrium of one game is far from every (approximate) equilibrium of any other game in the family.

Includes members:
  Member "better agnostic clustering via relaxed tensor norms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "list decodable subspace recovery")
  Member "sum of squares lower bounds for refuting any csp":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sum of squares meets square loss fast rates for agnostic tensor completion")
  Member "strongly refuting random csps below the spectral threshold":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "how to refute a random csp":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "hypercontractivity sum of squares proofs and their applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sum of squares bounds for the ordering principle")
  Member "approximability and proof complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficiently learning structured distributions from untrusted batches")
  Member "settling the complexity of computing two player nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "games of fixed rank a hierarchy of bimatrix games":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rank reduction in bimatrix games")

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)