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: "latent semantic learning with structured sparse representation for human action recognition"
  Abstract: This paper proposes a novel latent semantic learning method for extracting high-level latent semantics from a large vocabulary of abundant mid-level features (i.e. visual keywords) with structured sparse representation, which can help to bridge the semantic gap in the challenging task of human action recognition. To discover the manifold structure of mid-level features, we develop a graph-based spectral embedding approach to latent semantic learning, with the graph over mid-level features being constructed using sparse representation. Moreover, we define new L"1-norm hypergraph regularization to induce extra structured sparsity into sparse representation for graph construction. Due to the nice properties (sparsity and noise-robustness) of such structured sparse representation, our graph construction can capture dominant and robust relationships among mid-level features, which are crucial for the success of latent semantic learning in action recognition. Unlike the traditional latent semantic analysis based on topic models, our latent semantic learning method can explore the manifold structure of mid-level features in both graph construction and spectral embedding, which results in compact but discriminative high-level features. The experimental results on the commonly used KTH action dataset and unconstrained YouTube action dataset show the promising performance of our method.

Connected through hyperedge HE-1 (induced by source paper "face focused cross stream network for deception detection in videos"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "zero and few shot learning with semantic feature synthesis and competitive learning"
    - "a deep learning approach for multimodal deception detection"
    - "comparator networks"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "a simple neural network module for relational reasoning"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-2 (induced by source paper "learning zeroth class dictionary for human action recognition"):
  with members:
    - "joint distance maps based action recognition with convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "regularized maximum correntropy machine"):
  with members:
    - "a characterization of cybersecurity posture from network telescope data"
    - "semantic sparse recoding of visual content for image applications"
    - "document clustering based on max correntropy non negative matrix factorization"
    - "graph regularized non negative matrix factorization by maximizing correntropy"
    - "maximum mutual information regularized classification"
    - "an evasion and counter evasion study in malicious websites detection"
    - "adaptive learning of region based plsa model for total scene annotation"
    - "multiple graph regularized protein domain ranking"

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.MM(Multimedia)