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 "innovation representation of stochastic processes with application to causal inference"):
  Abstract: Typically, real-world stochastic processes are not easy to analyze. In this work we study the representation of any stochastic process as a memoryless innovation process triggering a dynamic system. We show that such a representation is always feasible for innovation processes taking values over a continuous set. However, the problem becomes more challenging when the alphabet size of the innovation is finite. In this case, we introduce both lossless and lossy frameworks, and provide closed-form solutions and practical algorithmic methods. In addition, we discuss the properties and uniqueness of our suggested approach. Finally, we show that the innovation representation problem has many applications. We focus our attention to Entropic Causal Inference, which has recently demonstrated promising performance, compared to alternative methods.

Includes members:
  Member "on the universality of the logistic loss function":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "lossless and lossy compression of random forests":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "teacher student compression with generative adversarial networks")
  Member "bregman divergence bounds and the universality of the logarithmic loss":
    also connected through other hyperedges:
      (none)
  Member "entropic causal inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "minimum entropy couplings and their applications")
  Member "large alphabet source coding using independent component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rediscovering deep neural networks through finite state distributions")
  Member "learning causal graphs with small interventions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hypothesis driven skill discovery for hierarchical deep reinforcement learning")
  Member "generalized independent component analysis over finite alphabets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on universal features for high dimensional learning and inference")
  Member "distinguishing cause from effect using observational data methods and benchmarks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "causal discovery from incomplete data a deep learning approach")

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)