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: "detecting correlations with little memory and communication"
  Abstract: We study the problem of identifying correlations in multivariate data, under information constraints: Either on the amount of memory that can be used by the algorithm, or the amount of communication when the data is distributed across several machines. We prove a tight trade-off between the memory/communication complexity and the sample complexity, implying (for example) that to detect pairwise correlations with optimal sample complexity, the number of required memory/communication bits is at least quadratic in the dimension. Our results substantially improve those of Shamir [2014], which studied a similar question in a much more restricted setting. To the best of our knowledge, these are the first provable sample/memory/communication trade-offs for a practical estimation problem, using standard distributions, and in the natural regime where the memory/communication budget is larger than the size of a single data point. To derive our theorems, we prove a new information-theoretic result, which may be relevant for studying other information-constrained learning problems.

Connected through hyperedge HE-1 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg"):
  with members:
    - "memory sample tradeoffs for linear regression with small error"
    - "communication and memory efficient testing of discrete distributions"
    - "extractor based time space lower bounds for learning"
    - "mixing complexity and its applications to neural networks"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "information theoretically secure databases"
    - "strongly refuting random csps below the spectral threshold"
    - "how to refute a random csp"

Connected through hyperedge HE-2 (induced by source paper "towards a combinatorial characterization of bounded memory learning"):
  with members:
    - "memory sample tradeoffs for linear regression with small error"
    - "space lower bounds for linear prediction in the streaming model"
    - "extractor based time space lower bounds for learning"
    - "a general memory bounded learning algorithm"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "a complete characterization of statistical query learning with applications to evolvability"

Connected through hyperedge HE-3 (induced by source paper "communication complexity in locally private distribution estimation and heavy hitters"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "distributed simulation and distributed inference"
    - "linear queries estimation with local differential privacy"
    - "test without trust optimal locally private distribution testing"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "practical locally private heavy hitters"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"

Connected through hyperedge HE-4 (induced by source paper "distributed learning with sublinear communication"):
  with members:
    - "introduction to online convex optimization"
    - "space lower bounds for linear prediction"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "communication compression for decentralized training"
    - "distributed simulation and distributed inference"
    - "sparsified sgd with memory"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "signsgd compressed optimisation for non convex problems"

Connected through hyperedge HE-5 (induced by source paper "estimating entropy of distributions in constant space"):
  with members:
    - "communication and memory efficient testing of discrete distributions"
    - "efficient profile maximum likelihood for universal symmetric property estimation"
    - "minimax optimal estimators for additive scalar functionals of discrete distributions"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-6 (induced by source paper "memory sample tradeoffs for linear regression with small error"):
  with members:
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "extractor based time space lower bounds for learning"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "a concentration theorem for projections"

Connected through hyperedge HE-7 (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"
    - "learning without interaction requires separation"
    - "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-8 (induced by source paper "fast learning requires good memory a time space lower bound for parity learning"):
  with members:
    - "extractor based time space lower bounds for learning"
    - "information theoretically secure databases"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"

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)