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 "tolerant junta testing and the connection to submodular optimization and function isomorphism"):
  Abstract: A function $f\colon \{-1,1\}^n \to \{-1,1\}$ is a $k$-junta if it depends on at most $k$ of its variables. We consider the problem of tolerant testing of $k$-juntas, where the testing algorithm must accept any function that is $\epsilon$-close to some $k$-junta and reject any function that is $\epsilon'$-far from every $k'$-junta for some $\epsilon'= O(\epsilon)$ and $k' = O(k)$. #R##N#Our first result is an algorithm that solves this problem with query complexity polynomial in $k$ and $1/\epsilon$. This result is obtained via a new polynomial-time approximation algorithm for submodular function minimization (SFM) under large cardinality constraints, which holds even when only given an approximate oracle access to the function. #R##N#Our second result considers the case where $k'=k$. We show how to obtain a smooth tradeoff between the amount of tolerance and the query complexity in this setting. Specifically, we design an algorithm that given $\rho\in(0,1/2)$ accepts any function that is $\frac{\epsilon\rho}{16}$-close to some $k$-junta and rejects any function that is $\epsilon$-far from every $k$-junta. The query complexity of the algorithm is $O\big( \frac{k\log k}{\epsilon\rho(1-\rho)^k} \big)$. #R##N#Finally, we show how to apply the second result to the problem of tolerant isomorphism testing between two unknown Boolean functions $f$ and $g$. We give an algorithm for this problem whose query complexity only depends on the (unknown) smallest $k$ such that either $f$ or $g$ is close to being a $k$-junta.

Includes members:
  Member "lower bounds for tolerant junta and unateness testing via rejection sampling of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating the distance to monotonicity of boolean functions")
  Member "escaping the local minima via simulated annealing optimization of approximately convex functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "simulated tempering langevin monte carlo ii an improved proof using soft markov chain decomposition")
  Member "a faster cutting plane method and its implications for combinatorial and convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "tolerant testers of image properties":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hard properties with very short pcpps and their applications")
  Member "local reconstructors and tolerant testers for connectivity and diameter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems")
  Member "learning with submodular functions a convex optimization perspective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online non monotone dr submodular maximization")
  Member "submodular approximation sampling based algorithms and lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal approximation for unconstrained non submodular minimization")

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.DS(Data Structures and Algorithms)