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: "a probabilistic semi supervised approach to multi task human activity modeling"
  Abstract: Human behavior is a continuous stochastic spatio-temporal process which is governed by semantic actions and affordances as well as latent factors. Therefore, video-based human activity modeling is concerned with a number of tasks such as inferring current and future semantic labels, predicting future continuous observations as well as imagining possible future label and feature sequences. In this paper we present a semi-supervised probabilistic deep latent variable model that can represent both discrete labels and continuous observations as well as latent dynamics over time. This allows the model to solve several tasks at once without explicit fine-tuning. We focus here on the tasks of action classification, detection, prediction and anticipation as well as motion prediction and synthesis based on 3D human activity data recorded with Kinect. We further extend the model to capture hierarchical label structure and to model the dependencies between multiple entities, such as a human and objects. Our experiments demonstrate that our principled approach to human activity modeling can be used to detect current and anticipate future semantic labels and to predict and synthesize future label and feature sequences. When comparing our model to state-of-the-art approaches, which are specifically designed for e.g. action classification, we find that our probabilistic formulation outperforms or is comparable to these task specific models.

Connected through hyperedge HE-1 (induced by source paper "a variational auto encoder model for stochastic point processes"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"
    - "time perception machine temporal point processes for the when where and what of activity prediction"
    - "when will you do what anticipating temporal occurrences of activities"
    - "disentangled sequential autoencoder"
    - "classify predict detect anticipate and synthesize hierarchical recurrent latent variable models for human activity modeling"
    - "toward controlled generation of text"

Connected through hyperedge HE-2 (induced by source paper "imitating by generating deep generative models for imitation of interactive tasks"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "classify predict detect anticipate and synthesize hierarchical recurrent latent variable models for human activity modeling"
    - "advances in variational inference"
    - "deep predictive policy training using reinforcement learning"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "density estimation using real nvp"

Connected through hyperedge HE-3 (induced by source paper "invariant feature mappings for generalizing affordance understanding using regularized metric learning"):
  with members:
    - "grounded human object interaction hotspots from video"
    - "classify predict detect anticipate and synthesize hierarchical recurrent latent variable models for human activity modeling"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "the curious robot learning visual representations via physical interactions"
    - "caffe convolutional architecture for fast feature embedding"

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.CV(Computer Vision and Pattern Recognition)