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: "exact soft confidence weighted learning"
  Abstract: In this paper, we propose a new Soft Confidence-Weighted (SCW) online learning scheme, which enables the conventional confidence-weighted learning method to handle non-separable cases. Unlike the previous confidence-weighted learning algorithms, the proposed soft confidence-weighted learning method enjoys all the four salient properties: (i) large margin training, (ii) confidence weighting, (iii) capability to handle non-separable data, and (iv) adaptive margin. Our experimental results show that the proposed SCW algorithms significantly outperform the original CW algorithm. When comparing with a variety of state-of-the-art algorithms (including AROW, NAROW and NHERD), we found that SCW generally achieves better or at least comparable predictive accuracy, but enjoys significant advantage of computational efficiency (i.e., smaller number of updates and lower time cost).

Connected through hyperedge HE-1 (induced by source paper "g softmax improving intra class compactness and inter class separability of features"):
  with members:
    - "snapshot ensembles train 1 get m for free"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "order free rnn with visual attention for multi label classification"
    - "miml fcn multi instance multi label learning via fully convolutional networks with privileged information"
    - "dilated residual networks"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "improving pairwise ranking for multi label image classification"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "asynchronous online federated learning for edge devices"):
  with members:
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated learning strategies for improving communication efficiency"
    - "leaf a benchmark for federated settings"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "federated learning for mobile keyboard prediction"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "federated learning with non iid data"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "detecting cyberattacks in industrial control systems using online learning algorithms"):
  with members:
    - "intelligent reflecting surface irs enabled covert communications in wireless networks"
    - "online learning a comprehensive survey"
    - "cyber insurance for heterogeneous wireless networks"
    - "sol a library for scalable online learning algorithms"
    - "hierarchical cooperation for operator controlled device to device communications a layered coalitional game approach"
    - "economics of internet of things iot an information market approach"
    - "payoff allocation of service coalition in wireless mesh network a cooperative game perspective"
    - "adaptive power management for wireless base stations in a smart grid environment"

Connected through hyperedge HE-4 (induced by source paper "adaptive cost sensitive online classification"):
  with members:
    - "online learning a comprehensive survey"
    - "efficient second order online learning by sketching"
    - "frequent directions simple and deterministic matrix sketching"
    - "sketching as a tool for numerical linear algebra"
    - "simple and deterministic matrix sketching"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-5 (induced by source paper "online multiclass classification based on prediction margin for partial feedback"):
  with members:
    - "on multilabel classification and ranking with partial feedback"

Connected through hyperedge HE-6 (induced by source paper "fully implicit online learning"):
  with members:
    - "introduction to online convex optimization"
    - "online bayesian passive aggressive learning"
    - "a unified view of regularized dual averaging and mirror descent with implicit updates"

Connected through hyperedge HE-7 (induced by source paper "confidence weighted bipartite ranking"):
  with members:
    - "top rank optimization in linear time"
    - "one pass auc optimization"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"

Connected through hyperedge HE-8 (induced by source paper "identifying actionable messages on social media"):
  with members:
    - "when to post on social networks"

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)