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: "hierarchical compound poisson factorization"
  Abstract: Non-negative matrix factorization models based on a hierarchical Gamma-Poisson structure capture user and item behavior effectively in extremely sparse data sets, making them the ideal choice for collaborative filtering applications. Hierarchical Poisson factorization (HPF) in particular has proved successful for scalable recommendation systems with extreme sparsity. HPF, however, suffers from a tight coupling of sparsity model (absence of a rating) and response model (the value of the rating), which limits the expressiveness of the latter. Here, we introduce hierarchical compound Poisson factorization (HCPF) that has the favorable Gamma-Poisson structure and scalability of HPF to high-dimensional extremely sparse matrices. More importantly, HCPF decouples the sparsity model from the response model, allowing us to choose the most suitable distribution for the response. HCPF can capture binary, non-negative discrete, non-negative continuous, and zero-inflated continuous responses. We compare HCPF with HPF on nine discrete and three continuous data sets and conclude that HCPF captures the relationship between sparsity and response better than HPF.

Connected through hyperedge HE-1 (induced by source paper "binary component decomposition part i the positive semidefinite case"):
  with members:
    - "on the restricted isometry property of centered self khatri rao products"
    - "binary component decomposition part ii the asymmetric case"
    - "improved scaling law for activity detection in massive mimo systems"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "structured low rank matrix factorization global optimality algorithms and applications"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "convex relaxations of structured matrix factorizations"

Connected through hyperedge HE-2 (induced by source paper "negative binomial matrix factorization for recommender systems"):
  with members:
    - "coupled compound poisson factorization"
    - "dynamic collaborative filtering with compound poisson factorization"
    - "pu learning for matrix completion"

Connected through hyperedge HE-3 (induced by source paper "coupled compound poisson factorization"):
  with members:
    - "classification with low rank and missing data"
    - "scalable recommendation with poisson factorization"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"
    - "linear regression with limited observation"
    - "efficient learning with partially observed attributes"

Connected through hyperedge HE-4 (induced by source paper "dynamic collaborative filtering with compound poisson factorization"):
  with members:
    - "dynamic poisson factorization"
    - "scalable recommendation with poisson factorization"
    - "dynamic matrix factorization a state space approach"

Connected through hyperedge HE-5 (induced by source paper "adacluster adaptive clustering for heterogeneous data"):
  with members:
    - "revisiting k means new algorithms via bayesian nonparametrics"

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)