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: "on probabilistic checking in perfect zero knowledge"
  Abstract: We present the first constructions of single-prover proof systems that achieve perfect zero knowledge (PZK) for languages beyond NP, under no intractability assumptions: #R##N#1. The complexity class #P has PZK proofs in the model of Interactive PCPs (IPCPs) [KR08], where the verifier first receives from the prover a PCP and then engages with the prover in an Interactive Proof (IP). #R##N#2. The complexity class NEXP has PZK proofs in the model of Interactive Oracle Proofs (IOPs) [BCS16,RRR16], where the verifier, in every round of interaction, receives a PCP from the prover. #R##N#Our constructions rely on succinct simulators that enable us to "simulate beyond NP", achieving exponential savings in efficiency over [BCGV16]. These simulators crucially rely on solving a problem that lies at the intersection of coding theory, linear algebra, and computational complexity, which we call the succinct constraint detection problem, and consists of detecting dual constraints with polynomial support size for codes of exponential block length. Our two results rely on solutions to this problem for fundamental classes of linear codes: #R##N#* An algorithm to detect constraints for Reed--Muller codes of exponential length. #R##N#* An algorithm to detect constraints for PCPs of Proximity of Reed--Solomon codes [BS08] of exponential degree. #R##N#The first algorithm exploits the Raz--Shpilka [RS05] deterministic polynomial identity testing algorithm, and shows, to our knowledge, a first connection of algebraic complexity theory with zero knowledge. Along the way, we give a perfect zero knowledge analogue of the celebrated sumcheck protocol [LFKN92], by leveraging both succinct constraint detection and low-degree testing. The second algorithm exploits the recursive structure of the PCPs of Proximity to show that small-support constraints are "locally" spanned by a small number of small-support constraints.

Connected through hyperedge HE-1 (induced by source paper "deep fri sampling outside the box improves soundness"):
  with members:
    - "every list decodable code for high noise has abundant near optimal rate puncturings"

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)