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: "sketching linear classifiers over data streams"
  Abstract: We introduce a new sub-linear space sketch---the Weight-Median Sketch---for learning compressed linear classifiers over data streams while supporting the efficient recovery of large-magnitude weights in the model. This enables memory-limited execution of several statistical analyses over streams, including online feature selection, streaming data explanation, relative deltoid detection, and streaming estimation of pointwise mutual information. Unlike related sketches that capture the most frequently-occurring features (or items) in a data stream, the Weight-Median Sketch captures the features that are most discriminative of one stream (or class) compared to another. The Weight-Median Sketch adopts the core data structure used in the Count-Sketch, but, instead of sketching counts, it captures sketched gradient updates to the model parameters. We provide a theoretical analysis that establishes recovery guarantees for batch and online learning, and demonstrate empirical improvements in memory-accuracy trade-offs over alternative memory-budgeted methods, including count-based sketches and feature hashing.

Connected through hyperedge HE-1 (induced by source paper "compressing gradient optimizers via count sketches"):
  with members:
    - "large scale language modeling converging on 40gb of text in four hours"
    - "extreme classification in log memory"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "hash embeddings for efficient word representations"
    - "breaking the softmax bottleneck a high rank rnn language model"

Connected through hyperedge HE-2 (induced by source paper "communication efficient distributed sgd with sketching"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "federated learning strategies for improving communication efficiency"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "measuring the effects of data parallelism on neural network training"

Connected through hyperedge HE-3 (induced by source paper "fast concurrent data sketches"):
  with members:
    - "distributionally linearizable data structures"
    - "multiqueues simpler faster and better relaxed concurrent priority queues"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "linearizable implementations do not suffice for randomized distributed computation"

Connected through hyperedge HE-4 (induced by source paper "macrobase prioritizing attention in fast data"):
  with members:
    - "drop dimensionality reduction optimization for time series"
    - "asap prioritizing attention via time series smoothing"
    - "noscope optimizing neural network queries over video at scale"
    - "building efficient query engines in a high level language"
    - "incremental knowledge base construction using deepdive"
    - "graph based anomaly detection and description a survey"
    - "towards a unified architecture for in rdbms analytics"
    - "identifying the signs of fraudulent accounts using data mining techniques"

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)