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: "almost optimal distribution free junta testing"
  Abstract: We consider the problem of testing whether an unknown $n$-variable Boolean function is a $k$-junta in the distribution-free property testing model, where the distance between function is measured with respect to an arbitrary and unknown probability distribution over $\{0,1\}^n$. Chen, Liu, Servedio, Sheng and Xie showed that the distribution-free $k$-junta testing can be performed, with one-sided error, by an adaptive algorithm that makes $\tilde O(k^2)/\epsilon$ queries. In this paper, we give a simple two-sided error adaptive algorithm that makes $\tilde O(k/\epsilon)$ queries.

Connected through hyperedge HE-1 (induced by source paper "almost optimal testers for concise representations"):
  with members:
    - "distribution free junta testing"
    - "near log convexity of measured heat in discrete time and consequences"
    - "exact learning from an honest teacher that answers membership queries"
    - "exact learning of juntas from membership queries"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "testing unateness of real valued functions"
    - "an widetilde o n queries adaptive tester for unateness"

Connected through hyperedge HE-2 (induced by source paper "distribution free testing of linear functions on r n"):
  with members:
    - "is your function low dimensional"
    - "testing halfspaces over rotation invariant distributions"
    - "sample based high dimensional convexity testing"
    - "testing properties of functions on finite groups"
    - "tight bounds for the distribution free testing of monotone conjunctions"
    - "active property testing"
    - "partially symmetric functions are efficiently isomorphism testable"

Connected through hyperedge HE-3 (induced by source paper "testing noisy linear functions for sparsity"):
  with members:
    - "estimating learnability in the sublinear data regime"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"
    - "testing low complexity affine invariant properties"
    - "lower bounds for sparse recovery"
    - "a unified framework for testing linear invariant properties"

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.DS(Data Structures and Algorithms)