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: "partially symmetric functions are efficiently isomorphism testable"
  Abstract: Given a function f: {0,1}^n \to {0,1}, the f-isomorphism testing problem requires a randomized algorithm to distinguish functions that are identical to f up to relabeling of the input variables from functions that are far from being so. An important open question in property testing is to determine for which functions f we can test f-isomorphism with a constant number of queries. Despite much recent attention to this question, essentially only two classes of functions were known to be efficiently isomorphism testable: symmetric functions and juntas. #R##N#We unify and extend these results by showing that all partially symmetric functions---functions invariant to the reordering of all but a constant number of their variables---are efficiently isomorphism-testable. This class of functions, first introduced by Shannon, includes symmetric functions, juntas, and many other functions as well. We conjecture that these functions are essentially the only functions efficiently isomorphism-testable. #R##N#To prove our main result, we also show that partial symmetry is efficiently testable. In turn, to prove this result we had to revisit the junta testing problem. We provide a new proof of correctness of the nearly-optimal junta tester. Our new proof replaces the Fourier machinery of the original proof with a purely combinatorial argument that exploits the connection between sets of variables with low influence and intersecting families. #R##N#Another important ingredient in our proofs is a new notion of symmetric influence. We use this measure of influence to prove that partial symmetry is efficiently testable and also to construct an efficient sample extractor for partially symmetric functions. We then combine the sample extractor with the testing-by-implicit-learning approach to complete the proof that partially symmetric functions are efficiently isomorphism-testable.

Connected through hyperedge HE-1 (induced by source paper "distribution free testing of linear functions on r n"):
  with members:
    - "almost optimal distribution free junta testing"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a characterization of constant sample testable properties"):
  with members:
    - "on active and passive testing"
    - "constant time testing and learning of image properties"
    - "trading query complexity for sample based testing and multi testing scalability"
    - "a characterization of locally testable affine invariant properties via decomposition theorems"
    - "new algorithms and lower bounds for monotonicity testing"
    - "active property testing"
    - "a unified framework for testing linear invariant properties"

Connected through hyperedge HE-3 (induced by source paper "testing submodularity and other properties of valuation functions"):
  with members:
    - "analysis of boolean functions"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "learning coverage functions and private release of marginals"
    - "learning valuation functions"
    - "is submodularity testable"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-4 (induced by source paper "on active and passive testing"):
  with members:
    - "active property testing"

Connected through hyperedge HE-5 (induced by source paper "testing properties of functions on finite groups"):
  with members:
    - "a characterization of locally testable affine invariant properties via decomposition theorems"
    - "estimating the distance from testable affine invariant properties"
    - "every locally characterized affine invariant property is testable"
    - "testing low complexity affine invariant properties"

Connected through hyperedge HE-6 (induced by source paper "a characterization of locally testable affine invariant properties via decomposition theorems"):
  with members:
    - "estimating the distance from testable affine invariant properties"
    - "every locally characterized affine invariant property is testable"
    - "testing low complexity affine invariant properties"
    - "a unified framework for testing linear invariant properties"

Connected through hyperedge HE-7 (induced by source paper "local correction with constant error rate"):
  with members:
    - "local correction of juntas"

Connected through hyperedge HE-8 (induced by source paper "a unified framework for testing linear invariant properties"):
  with members:
    - "every locally characterized affine invariant property is testable"
    - "testing low complexity affine invariant properties"
    - "separations of matroid freeness properties"
    - "succinct representation of codes with applications to testing"
    - "adaptive simulated annealing a near optimal connection between sampling and counting"

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)