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: "optimal unateness testers for real valued functions adaptivity helps"
  Abstract: We study the problem of testing unateness of functions $f:\{0,1\}^d \to \mathbb{R}.$ We give a $O(\frac{d}{\epsilon} \cdot \log\frac{d}{\epsilon})$-query nonadaptive tester and a $O(\frac{d}{\epsilon})$-query adaptive tester and show that both testers are optimal for a fixed distance parameter $\epsilon$. Previously known unateness testers worked only for Boolean functions, and their query complexity had worse dependence on the dimension both for the adaptive and the nonadaptive case. Moreover, no lower bounds for testing unateness were known. We also generalize our results to obtain optimal unateness testers for functions $f:[n]^d \to \mathbb{R}$. #R##N#Our results establish that adaptivity helps with testing unateness of real-valued functions on domains of the form $\{0,1\}^d$ and, more generally, $[n]^d$. This stands in contrast to the situation for monotonicity testing where there is no adaptivity gap for functions $f:[n]^d \to \mathbb{R}$.

Connected through hyperedge HE-1 (induced by source paper "approximating the distance to monotonicity of boolean functions"):
  with members:
    - "testing unateness nearly optimally"
    - "junta correlation is testable"
    - "lower bounds for tolerant junta and unateness testing via rejection sampling of graphs"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"

Connected through hyperedge HE-2 (induced by source paper "testing unateness nearly optimally"):
  with members:
    - "distribution free junta testing"
    - "adaptive boolean monotonicity testing in total influence time"
    - "a lower bound for nonadaptive one sided error testing of unateness of boolean functions over the hypercube"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "testing unateness of real valued functions"
    - "a polynomial lower bound for testing monotonicity"
    - "tight bounds for the distribution free testing of monotone conjunctions"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"

Connected through hyperedge HE-3 (induced by source paper "lower bounds for tolerant junta and unateness testing via rejection sampling of graphs"):
  with members:
    - "a lower bound for nonadaptive one sided error testing of unateness of boolean functions over the hypercube"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "a widetilde o n non adaptive tester for unateness"
    - "a polynomial lower bound for testing monotonicity"
    - "testing probability distributions using conditional samples"
    - "on the power of conditional samples in distribution testing"

Connected through hyperedge HE-4 (induced by source paper "boolean unateness testing with widetilde o n 3 4 adaptive queries"):
  with members:
    - "a lower bound for nonadaptive one sided error testing of unateness of boolean functions over the hypercube"
    - "an adaptivity hierarchy theorem for property testing"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "testing unateness of real valued functions"
    - "a widetilde o n non adaptive tester for unateness"
    - "a polynomial lower bound for testing monotonicity"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"

Connected through hyperedge HE-5 (induced by source paper "adaptivity is exponentially powerful for testing monotonicity of halfspaces"):
  with members:
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "a polynomial lower bound for testing monotonicity"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"
    - "an optimal lower bound for monotonicity testing over hypergrids"
    - "a o n monotonicity tester for boolean functions over the hypercube"
    - "optimal bounds for monotonicity and lipschitz testing over hypercubes and hypergrids"

Connected through hyperedge HE-6 (induced by source paper "beyond talagrand functions new lower bounds for testing monotonicity and unateness"):
  with members:
    - "testing unateness of real valued functions"
    - "a widetilde o n non adaptive tester for unateness"
    - "a polynomial lower bound for testing monotonicity"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"
    - "an optimal lower bound for monotonicity testing over hypergrids"
    - "a o n monotonicity tester for boolean functions over the hypercube"
    - "optimal bounds for monotonicity and lipschitz testing over hypercubes and hypergrids"

Connected through hyperedge HE-7 (induced by source paper "testing piecewise functions"):
  with members:
    - "the optimal sample complexity of pac learning"
    - "active property testing"

Connected through hyperedge HE-8 (induced by source paper "a lower bound for nonadaptive one sided error testing of unateness of boolean functions over the hypercube"):
  with members:
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"

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)