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 "learning without interaction requires separation"):
  Abstract: One of the key resources in large-scale learning systems is the number of rounds of communication between the server and the clients holding the data points. We study this resource for systems with two types of constraints on the communication from each of the clients: local differential privacy and limited number of bits communicated. For both models the number of rounds of communications is captured by the number of rounds of interaction when solving the learning problem in the statistical query (SQ) model. For many learning problems known efficient algorithms require many rounds of interaction. Yet little is known on whether this is actually necessary. In the context of classification in the PAC learning model, Kasiviswanathan et al. (2008) constructed an artificial class of functions that is PAC learnable with respect to a fixed distribution but cannot be learned by an efficient non-interactive (or one-round) SQ algorithm. Here we show that a similar separation holds for learning linear separators and decision lists without assumptions on the distribution. To prove this separation we show that non-interactive SQ algorithms can only learn function classes of low margin complexity, that is classes of functions that can be represented as large-margin linear separators.

Includes members:
  Member "an exponential speedup in parallel running time for submodular maximization without loss in approximation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "inference under information constraints i lower bounds from chi square contraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally private hypothesis selection")
  Member "distributed mean estimation with limited communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rpn a residual pooling network for efficient federated learning")
  Member "algorithmic stability for adaptive data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "preventing false discovery in interactive data analysis is hard":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "interactive fingerprinting codes and the hardness of preventing false discovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "statistical active learning algorithms for noise tolerance and differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locally private learning without interaction requires separation")
  Member "distribution independent evolvability of linear threshold functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "locally private learning without interaction requires separation")

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.LG(Machine Learning)