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: "the optimality of correlated sampling"
  Abstract: In the "correlated sampling" problem, two players, say Alice and Bob, are given two distributions, say $P$ and $Q$ respectively, over the same universe and access to shared randomness. The two players are required to output two elements, without any interaction, sampled according to their respective distributions, while trying to minimize the probability that their outputs disagree. A well-known protocol due to Holenstein, with close variants (for similar problems) due to Broder, and to Kleinberg and Tardos, solves this task with disagreement probability at most $2 \delta/(1+\delta)$, where $\delta$ is the total variation distance between $P$ and $Q$. This protocol has been used in several different contexts including sketching algorithms, approximation algorithms based on rounding linear programming relaxations, the study of parallel repetition and cryptography. #R##N#In this note, we give a surprisingly simple proof that this protocol is in fact tight. Specifically, for every $\delta \in (0,1)$, we show that any correlated sampling scheme should have disagreement probability at least $2\delta/(1+\delta)$. This partially answers a recent question of Rivest. #R##N#Our proof is based on studying a new problem we call "constrained agreement". Here, Alice is given a subset $A \subseteq [n]$ and is required to output an element $i \in A$, Bob is given a subset $B \subseteq [n]$ and is required to output an element $j \in B$, and the goal is to minimize the probability that $i \neq j$. We prove tight bounds on this question, which turn out to imply tight bounds for correlated sampling. Though we settle basic questions about the two problems, our formulation also leads to several questions that remain open.

Connected through hyperedge HE-1 (induced by source paper "communication for generating correlation a unifying survey"):
  with members:
    - "secret key capacity for multipleaccess channel with public feedback"
    - "exact exponent for soft covering"
    - "exact channel synthesis"
    - "strong converse using change of measure arguments"
    - "coding for positive rate in the source model key agreement problem"
    - "wiretap channels nonasymptotic fundamental limits"
    - "channel resolvability theorems for general sources and channels"
    - "strong functional representation lemma and applications to coding theorems"

Connected through hyperedge HE-2 (induced by source paper "communication for generating correlation a survey"):
  with members:
    - "secret key capacity for multipleaccess channel with public feedback"
    - "exact soft covering exponent"
    - "on exact and infty r enyi common informations"
    - "exact channel synthesis"
    - "strong converse using change of measure arguments"
    - "coding for positive rate in the source model key agreement problem"
    - "wiretap channels nonasymptotic fundamental limits"
    - "channel resolvability theorems for general sources and channels"

Connected through hyperedge HE-3 (induced by source paper "inference under information constraints ii communication constraints and shared randomness"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "test without trust optimal locally private distribution testing"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "sample optimal identity testing with high probability"
    - "a new approach for testing properties of discrete distributions"
    - "information theoretic lower bounds on bayes risk in decentralized estimation"
    - "decidability of non interactive simulation of joint distributions"

Connected through hyperedge HE-4 (induced by source paper "multi agent common knowledge reinforcement learning"):
  with members:
    - "the starcraft multi agent challenge"
    - "reasoning about hypothetical agent behaviours and their parameters"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning to communicate implicitly by actions"
    - "cooperative and distributed reinforcement learning of drones for field coverage"
    - "multi vehicle flocking control with deep deterministic policy gradient method"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "agreeing to cross how drivers and pedestrians communicate"

Connected through hyperedge HE-5 (induced by source paper "distributed simulation and distributed inference"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "test without trust optimal locally private distribution testing"
    - "sample optimal identity testing with high probability"
    - "information theoretic lower bounds on bayes risk in decentralized estimation"
    - "neyman pearson test for zero rate multiterminal hypothesis testing"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "testing shape restrictions of discrete distributions"
    - "testing identity of structured distributions"

Connected through hyperedge HE-6 (induced by source paper "optimal densification for fast and accurate minwise hashing"):
  with members:
    - "ssh sketch shingle hash for indexing massive scale time series"
    - "consistent weighted sampling made fast small and easy"
    - "hashing for statistics over k partitions"

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)