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: "polynomially low error pcps with polyloglog n queries via modular composition"
  Abstract: We show that every language in NP has a PCP verifier that tosses O(log n) random coins, has perfect completeness, and a soundness error of at most 1/poly(n), while making O(poly log log n) queries into a proof over an alphabet of size at most n1/poly log log n. Previous constructions that obtain 1/poly(n) soundness error used either poly log n queries or an exponential alphabet, i.e. of size 2nc for some c> 0. Our result is an exponential improvement in both parameters simultaneously. Our result can be phrased as polynomial-gap hardness for approximate CSPs with arity poly log log n and alphabet size n1/poly log n. The ultimate goal, in this direction, would be to prove polynomial hardness for CSPs with constant arity and polynomial alphabet size (aka the sliding scale conjecture for inverse polynomial soundness error).   Our construction is based on a modular generalization of previous PCP constructions in this parameter regime, which involves a composition theorem that uses an extra 'consistency' query but maintains the inverse polynomial relation between the soundness error and the alphabet size.   Our main technical/conceptual contribution is a new notion of soundness, which we refer to as distributional soundness, that replaces the previous notion of "list decoding soundness", and allows us to invoke composition a super-constant number of times without incurring a blow-up in the soundness error.

Connected through hyperedge HE-1 (induced by source paper "nearly tight bounds for robust proper learning of halfspaces with a margin"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "degree d chow parameters robustly determine degree d ptfs and algorithmic applications"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"

Connected through hyperedge HE-2 (induced by source paper "eth hardness of approximating 2 csps and directed steiner network"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "improved approximation algorithms for projection games"
    - "distributed pcp theorems for hardness of approximation in p"
    - "detecting communities is hard and counting them is even harder"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "approximating spanners and directed steiner forest upper and lower bounds"
    - "approximation of non boolean 2csp"

Connected through hyperedge HE-3 (induced by source paper "the computational complexity of training relu s"):
  with members:
    - "complexity of training relu neural networks"
    - "reliably learning the relu in polynomial time"
    - "breaking the curse of dimensionality with convex 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)