human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "info clustering a mathematical theory for data clustering"):
  Abstract: We formulate an info-clustering paradigm based on a multivariate mutual information measure that naturally extends Shannon’s mutual information between two random variables to the multivariate case involving more than two random variables. With proper model reductions, we show that the paradigm can be applied to study the human genome and connectome in a more meaningful way than the conventional algorithmic approach. Not only can it provide justifications and refinements to some existing techniques, but it also inspires new computationally feasible solutions.

Includes members:
  Member "bounds on the communication rate needed to achieve sk capacity in the hypergraphical source model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secret key agreement for hypergraphical sources with limited total discussion")
  Member "duality between feature selection and data clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed secret key agreement maximizing multivariate mutual information per bit")
  Member "fairness in communication for omniscience":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication for omniscience")
  Member "incremental and decremental secret key agreement":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secret key agreement for hypergraphical sources with limited total discussion")
  Member "on the public communication needed to achieve sk capacity in the multiterminal source model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication for generating correlation a unifying survey")
  Member "iterative merging algorithm for cooperative data exchange":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "part ii a practical approach for successive omniscience")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "the communication complexity of achieving sk capacity in a class of pin models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "wiretap secret key capacity of tree pin")

Question: Please tell me which class the source paper 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)