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: "l p testing and learning of discrete distributions"
  Abstract: The classic problems of testing uniformity of and learning a discrete distribution, given access to independent samples from it, are examined under general lp metrics. The intuitions and results often contrast with the classic l1 case. For p > 1, we can learn and test with a number of samples that is independent of the support size of the distribution: For 1    For some lp metrics, uniformity testing becomes easier over larger supports: a 6-sided die requires fewer trials to test for fairness than a 2-sided coin, and a card-shuffler requires fewer trials than the die. In fact, this inverse dependence on support size holds if and only if p > 4/3. The uniformity testing algorithm simply thresholds the number of "collisions" or "coincidences" and has an optimal sample complexity up to constant factors for all 1 ≤ p ≤ 2. Another algorithm gives order-optimal sample complexity for l∞ uniformity testing. Meanwhile, the most natural learning algorithm is shown to have order-optimal sample complexity for all lp metrics.   The author thanks Cement Canonne for discussions and contributions to this work.

Connected through hyperedge HE-1 (induced by source paper "anaconda a non adaptive conditional sampling algorithm for distribution testing"):
  with members:
    - "generalized uniformity testing"
    - "which distribution distances are sublinearly testable"
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "improving and extending the testing of distributions for shape restricted properties"
    - "faster sublinear algorithms using conditional sampling"
    - "optimal testing for properties of distributions"
    - "big data on the rise testing monotonicity of distributions"

Connected through hyperedge HE-2 (induced by source paper "adaptive sampling for rapidly matching histograms"):
  with members:
    - "effortless data exploration with zenvisage an expressive and interactive visual analytics system"
    - "visualization aware sampling for very large databases"
    - "rapid sampling for visualizations with ordering guarantees"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "pf ola a high performance framework for parallel on line aggregation"
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "mining top k frequent itemsets through progressive sampling"

Connected through hyperedge HE-3 (induced by source paper "which distribution distances are sublinearly testable"):
  with members:
    - "a new approach for testing properties of discrete distributions"
    - "collision based testers are optimal for uniformity and closeness"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "optimal testing for properties of distributions"
    - "testing identity of structured distributions"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "testing closeness of discrete distributions"

Connected through hyperedge HE-4 (induced by source paper "testing ising models"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "regression from dependent observations"
    - "learning from weakly dependent data under dobrushin s condition"
    - "private identity testing for high dimensional distributions"
    - "learning and testing causal models with interventions"
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "testing identity of multidimensional histograms"
    - "near optimal closeness testing of discrete histogram distributions"

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)