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: "cross entropy clustering"
  Abstract: We build a general and easily applicable clustering theory, which we call crossentropy clustering (shortly CEC), which joins the advantages of classical kmeans (easy implementation and speed) with those of EM (ane invariance and ability to adapt to clusters of desired shapes). Moreover, contrary to k-means and EM, CEC nds the optimal number of clusters by automatically removing groups which have negative information cost.

Connected through hyperedge HE-1 (induced by source paper "efficient mixture model for clustering of sparse high dimensional binary data"):
  with members:
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "semi supervised cross entropy clustering with information bottleneck constraint"
    - "hyperplane clustering via dual principal component pursuit"
    - "innovation pursuit a new approach to subspace clustering"
    - "getting deep recommenders fit bloom embeddings for sparse binary input output networks"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "why should i trust you explaining the predictions of any classifier"
    - "entropy of the mixture of sources and entropy dimension"

Connected through hyperedge HE-2 (induced by source paper "semi supervised cross entropy clustering with information bottleneck constraint"):
  with members:
    - "a graph based semi supervised k nearest neighbor method for nonlinear manifold distributed data classification"
    - "semi supervised information maximization clustering"

Connected through hyperedge HE-3 (induced by source paper "semi supervised model based clustering with controlled clusters leakage"):
  with members:
    - "spherical wards clustering and generalized voronoi diagrams"

Connected through hyperedge HE-4 (induced by source paper "spherical wards clustering and generalized voronoi diagrams"):
  with members:
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-5 (induced by source paper "maximum entropy linear manifold for learning discriminative low dimensional representation"):
  with members:
    - "fast optimization of multithreshold entropy linear classifier"
    - "on the consistency of multithreshold entropy linear classifier"
    - "multithreshold entropy linear classifier"
    - "discriminative features via generalized eigenvectors"
    - "on the local correctness of l 1 minimization for dictionary learning"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-6 (induced by source paper "extreme entropy machines robust information theoretic classification"):
  with members:
    - "multithreshold entropy linear classifier"
    - "cluster based rbf kernel for support vector machines"
    - "large margin distribution machine"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-7 (induced by source paper "a local gaussian filter and adaptive morphology as tools for completing partially discontinuous curves"):
  with members:
    - "detection of elliptical shapes via cross entropy clustering"

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.IT(Information Theory)