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: "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.

Connected through hyperedge HE-1 (induced by source paper "the role of interactivity in local differential privacy"):
  with members:
    - "exponential separations in local differential privacy"
    - "the privacy blanket of the shuffle model"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "towards instance optimal private query release"
    - "test without trust optimal locally private distribution testing"
    - "locally private gaussian estimation"
    - "tight lower bounds for locally differentially private selection"
    - "inference under information constraints i lower bounds from chi square contraction"

Connected through hyperedge HE-2 (induced by source paper "interaction is necessary for distributed learning with privacy or communication constraints"):
  with members:
    - "inference under information constraints ii communication constraints and shared randomness"
    - "distributed learning with sublinear communication"
    - "locally private learning without interaction requires separation"
    - "estimating smooth glm in non interactive local differential privacy model with public unlabeled data"
    - "exponential separations in local differential privacy through communication complexity"
    - "the role of interactivity in local differential privacy"
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "hadamard response estimating distributions privately efficiently and with little communication"

Connected through hyperedge HE-3 (induced by source paper "estimating smooth glm in non interactive local differential privacy model with public unlabeled data"):
  with members:
    - "privately answering classification queries in the agnostic pac model"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "linear queries estimation with local differential privacy"
    - "noninteractive locally private learning of linear models via polynomial approximations"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "collecting telemetry data privately"
    - "learning privately from multiparty data"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-4 (induced by source paper "exponential separations in local differential privacy"):
  with members:
    - "the role of interactivity in local differential privacy"
    - "test without trust optimal locally private distribution testing"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "collecting telemetry data privately"
    - "local private efficient protocols for succinct histograms"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "simulating noisy channel interaction"

Connected through hyperedge HE-5 (induced by source paper "space lower bounds for linear prediction in the streaming model"):
  with members:
    - "memory sample tradeoffs for linear regression with small error"
    - "detecting correlations with little memory and communication"
    - "testing matrix rank optimally"
    - "extractor based time space lower bounds for learning"
    - "time space tradeoffs for learning from small test spaces learning low degree polynomial functions"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "on the communication complexity of linear algebraic problems in the message passing model"

Connected through hyperedge HE-6 (induced by source paper "exponential separations in local differential privacy through communication complexity"):
  with members:
    - "distributed differential privacy via shuffling"
    - "heavy hitters and the structure of local privacy"
    - "collecting telemetry data privately"
    - "simulating noisy channel interaction"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-7 (induced by source paper "locally private gaussian estimation"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "the role of interactivity in local differential privacy"
    - "differentially private hierarchical group size estimation"
    - "privately learning high dimensional distributions"
    - "differentially private hierarchical count of counts histograms"
    - "test without trust optimal locally private distribution testing"
    - "tight lower bounds for locally differentially private selection"
    - "locally private hypothesis testing"

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.LG(Machine Learning)