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: "duality between feature selection and data clustering"
  Abstract: The feature-selection problem is formulated from an information-theoretic perspective. We show that the problem can be efficiently solved by an extension of the recently proposed info-clustering paradigm. This reveals the fundamental duality between feature selection and data clustering,which is a consequence of the more general duality between the principal partition and the principal lattice of partitions in combinatorial optimization.

Connected through hyperedge HE-1 (induced by source paper "compressed secret key agreement maximizing multivariate mutual information per bit"):
  with members:
    - "secret key agreement under discussion rate constraints"
    - "opening the black box of deep neural networks via information"
    - "on the optimality of secret key agreement via omniscience"
    - "bounds on the communication rate needed to achieve sk capacity in the hypergraphical source model"
    - "a lattice coding scheme for secret key generation from gaussian markov tree sources"
    - "common randomness and key generation with limited interaction"
    - "info clustering a mathematical theory for data clustering"
    - "incremental and decremental secret key agreement"

Connected through hyperedge HE-2 (induced by source paper "on the optimality of secret key agreement via omniscience"):
  with members:
    - "secret key agreement under discussion rate constraints"
    - "compressed secret key agreement maximizing multivariate mutual information per bit"
    - "bounds on the communication rate needed to achieve sk capacity in the hypergraphical source model"
    - "info clustering a mathematical theory for data clustering"
    - "incremental and decremental secret key agreement"
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "the communication complexity of achieving sk capacity in a class of pin models"
    - "achieving sk capacity in the source model when must all terminals talk"

Connected through hyperedge HE-3 (induced by source paper "agglomerative info clustering"):
  with members:
    - "info clustering a mathematical theory for data clustering"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "on tightness of mutual dependence upperbound for secret key capacity of multiple terminals"

Connected through hyperedge HE-4 (induced by source paper "info clustering a mathematical theory for data clustering"):
  with members:
    - "bounds on the communication rate needed to achieve sk capacity in the hypergraphical source model"
    - "fairness in communication for omniscience"
    - "incremental and decremental secret key agreement"
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "iterative merging algorithm for cooperative data exchange"
    - "deep residual learning for image recognition"
    - "the communication complexity of achieving sk capacity in a class of pin models"
    - "parallel correlation clustering on big graphs"

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)