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: "classify predict detect anticipate and synthesize hierarchical recurrent latent variable models for human activity modeling"
  Abstract: Human activity modeling operates on two levels: high-level action modeling, such as classification, prediction, detection and anticipation, and low-level motion trajectory prediction and synthesis. In this work, we propose a semi-supervised generative latent variable model that addresses both of these levels by modeling continuous observations as well as semantic labels. We extend the model to capture the dependencies between different entities, such as a human and objects, and to represent hierarchical label structure, such as high-level actions and sub-activities. In the experiments we investigate our model's capability to classify, predict, detect and anticipate semantic action and affordance labels and to predict and generate human motion. We train our models on data extracted from depth image streams from the Cornell Activity 120, the UTKinect-Action3D and the Stony Brook University Kinect Interaction Dataset. We observe that our model performs well in all of the tasks and often outperforms 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"
    - "a probabilistic semi supervised approach to multi task 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"
    - "a probabilistic semi supervised approach to multi task 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"
    - "a probabilistic semi supervised approach to multi task 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)